{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [7.10542736e-17 1.66533454e-17 1.85962357e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029258328157158e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"notebook_images/batchnorm_graph.png\" width=691 height=202>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  6.284600172572596e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.79x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 4.05e-10\n",
      "b1 relative error: 2.22e-07\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 1.38e-08\n",
      "b2 relative error: 7.99e-07\n",
      "b3 relative error: 1.73e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 8.80e-09\n",
      "gamma2 relative error: 5.28e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340975\n",
      "(Epoch 0 / 10) train acc: 0.101000; val_acc: 0.112000\n",
      "(Epoch 1 / 10) train acc: 0.246000; val_acc: 0.215000\n",
      "(Iteration 21 / 200) loss: 1.914006\n",
      "(Epoch 2 / 10) train acc: 0.337000; val_acc: 0.290000\n",
      "(Iteration 41 / 200) loss: 2.119352\n",
      "(Epoch 3 / 10) train acc: 0.415000; val_acc: 0.302000\n",
      "(Iteration 61 / 200) loss: 1.934722\n",
      "(Epoch 4 / 10) train acc: 0.442000; val_acc: 0.298000\n",
      "(Iteration 81 / 200) loss: 1.528394\n",
      "(Epoch 5 / 10) train acc: 0.466000; val_acc: 0.298000\n",
      "(Iteration 101 / 200) loss: 1.404605\n",
      "(Epoch 6 / 10) train acc: 0.546000; val_acc: 0.322000\n",
      "(Iteration 121 / 200) loss: 1.355509\n",
      "(Epoch 7 / 10) train acc: 0.590000; val_acc: 0.325000\n",
      "(Iteration 141 / 200) loss: 1.553660\n",
      "(Epoch 8 / 10) train acc: 0.632000; val_acc: 0.321000\n",
      "(Iteration 161 / 200) loss: 0.823669\n",
      "(Epoch 9 / 10) train acc: 0.692000; val_acc: 0.336000\n",
      "(Iteration 181 / 200) loss: 1.100841\n",
      "(Epoch 10 / 10) train acc: 0.708000; val_acc: 0.329000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.155000; val_acc: 0.140000\n",
      "(Epoch 1 / 10) train acc: 0.182000; val_acc: 0.160000\n",
      "(Iteration 21 / 200) loss: 2.143171\n",
      "(Epoch 2 / 10) train acc: 0.212000; val_acc: 0.178000\n",
      "(Iteration 41 / 200) loss: 1.915787\n",
      "(Epoch 3 / 10) train acc: 0.244000; val_acc: 0.228000\n",
      "(Iteration 61 / 200) loss: 1.856857\n",
      "(Epoch 4 / 10) train acc: 0.279000; val_acc: 0.255000\n",
      "(Iteration 81 / 200) loss: 1.789520\n",
      "(Epoch 5 / 10) train acc: 0.290000; val_acc: 0.253000\n",
      "(Iteration 101 / 200) loss: 1.839935\n",
      "(Epoch 6 / 10) train acc: 0.337000; val_acc: 0.252000\n",
      "(Iteration 121 / 200) loss: 1.793666\n",
      "(Epoch 7 / 10) train acc: 0.370000; val_acc: 0.252000\n",
      "(Iteration 141 / 200) loss: 1.725922\n",
      "(Epoch 8 / 10) train acc: 0.359000; val_acc: 0.254000\n",
      "(Iteration 161 / 200) loss: 1.691867\n",
      "(Epoch 9 / 10) train acc: 0.405000; val_acc: 0.261000\n",
      "(Iteration 181 / 200) loss: 1.576471\n",
      "(Epoch 10 / 10) train acc: 0.415000; val_acc: 0.277000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TwrMftNNTvNPDSQiX+u64SilVRTQRVEoppZR/K8iHSTfDotHQ8wG44DkIq6JTmNia0PsfNiHs9bCTEPaEcYMhdUnVxKCUUj6giaBSSiml/Ff+QTtOb8VE6PMknPNPEKn6OGJrQK+HnITwH7DlF3j3bPhkEKQurvp4lFLqOGkiqJRSSin/lJsFn1wFa7+Fi1+20zy4LbYG9HrQFpXp/U/YOhfe7QVjB0LKQrejU0opr2kiqJRSSin/k7MPxlwKm3+By9+F04e7HVFxMYlw9gM2ITznn7B9Hrx3Doy9CrZrQqiU8n+aCCqllFLKv2Ttgg8vsUVZrh4DHQe6HVHZYhLsuMW7l9vpJ7YvgJHnwMdXwLb5bkd3bJaNh1fawxM17P2y8W5HpJTyAU0ElVJKKX8Tyifi6dvg/Qtg70a4ZjycdLHbEXknujr0uM+OITz3cUhZBKP6wJjLYds8t6Pz3rLxMPVOyNgGGHs/9c7Q+gwqFSLEGON2DJUiOTnZLFiwwO0wlFJKqeNTdCKen3N4WWQs9Hvdv1vGKkPaBhjdH3L3w5AJ0PRMtyM6drlZMH8k/Po6ZKdBy9622EzTs+zveOZTkLEdEhvblkS3freFhZB/wMablwUfXgxZO4/cLrEJ3LOi6uNTSh0VEVlojEn2altNBJVSSik/8kp7pzWmhGA/Ef9zBYy5DEwBDP0CGnZyO6LKkZsFC0bBL69D9h6o29a2dhbkHt7maBL9wgKbsOVmQd4ByNvv8XOWTaLznMe5WWWsP3B4H/kHvHwhAk+kH9NboJSqOkeTCPpwJlallFJKHbWM7Ue3PBhsX2DH1EXGwbCvoO4JbkdUeaKrQbe74PSbYMH78P1jYAqLb5OfA1/dZ7uT5mWVkeg5j/OzvT92VDXnFm/jiKoO1Rs6P1ez3Vmj4p2fnWXfPmwT1pLCwmHpp9D+CgiPPL73RCnlFzQRVEoppfxJbC3ISTtyeXgUrJ8Brc51Zx49X9k0B8YNgvg6MGwK1GzmdkS+ERUPXf8G3z1a+vrcTFj8sUeS5twnNHZ+jvdI3oqSu+pHJnpF20bGQ9gxloIo2TU5PAri6sAXt8AP/4Zud8Ip19qWTKVUwNJEUCmllPIX896zSaCEFW81CouEiFjbatawE3S/B9r2t600gWztdBg/DGo2h6GTIaGh2xH5XmJj/+76W9Q9teQYxvZXwrrv4OeX4ev7YfbzcNZttqUzJtHdmJVSx0THCCqllFJuM8aeWP/4LJx4ka2U+eNzxU/E210Kyz6DX16FtPVQq5XtcthpEEREu/0Kjt6KiTBpBNRvD9dOgvjabkdUNQK9GJAxsOVXmxCunwHRCZB8I3T5P6hWz+3olAp5WixGKaWUChSFhfDtgzDvXeh0DfT/L4SX02GnsABWT7Un4juWQvUkexJ+2vW2W2AgWPQRTLkTmnaBaz6zc/GFEn+qGno8diyFn1+BlZNt99FTrrXdRms2dzsypUKWJoJKKaVUIDiUB5NvgxWfQ5c7oO/T3o/rMgY2/GBPxDfPgdiacMYtcOYtEFfLt3Efj7n/g+kPQ+s+MHAMRMW5HZE6XmkbbEv1knG2S3P7K2z35frt3I5MqZCjiaBSSinl7/IO2PFx62dAnyeg293HXgRm23ybEK75yhYJOe1620qY2KgSAz5OxsDsF+DHZ+z4xitGQUSU21GpypSZCnPfhAUf2GkpTrgQetwLTc5wOzKlQoYmgkoppZQ/y94Ln1wNKQvgklfhtOsqZ7+7VsPPr8LyCbbgTKdBNsGs07py9n+sjIHv/glz34DOQ+x4uPK6vwa5yYtTeHH6GlLTc0iqEcsD55/Ipaf4UdJ+vLL32q7Ov78NOfugWXfocU/wVbxVyg9pIqiUUkr5q8xUGHM57N1gW8Xa9a/8Y+zbAr/+FxaPgUO59hjd74WkzpV/rIoUFsC0e2DRaNt19YLnjn1agyAweXEKD09aTk5+wV/LYiPDefbyDsGVDIJt9V442n4W96dCg462y2i7AYFf8VYpP6WJoFJKKeWP0jbAR5dCzl4Y9Am0PNu3x8vaBb+9BfNH2nnqWp1jE8Lm3aumZaYg3849t2Ii9LgfzvlnyLcIdXvuB1LSc45Y3qhGLL88dI4LEVWBQ3nBVfFWKT92NIlg6F6SU0opparSjqUw6jw7duq6qb5PAsGW8+/zuJ2frs8T8OcKGH0JjOoLf3xtK5b6Sv5B+GyoTQL7PAnnPhrySSBAailJYNHywsLguDh/hIgoOHUo/N88uGq0rW479U54rTP8+gbkZrkdoVIhSVsElVJKKV/bNAfGDYbYGjD0C6jTxp048nNgyVj45TVI3wp129queu2vqNwxe7lZ8Olg+7ovfslOOq4AOPOZGezMzC11XYOEGC7p2JD+nZPo0CgRCdbEOVAr3ioVALRrqFIqeOapUirQ/fEVTLjBzq029Av/qORZcAhWTrIn4rtWQY2m0PVOOw9cZOzx7TtnH4y9ClIWwaVvQaerKyfmIPBnxkEuev0n9h7IL7Y8JjKMq5KbsCP9ILPX7iK/wNC8dhz9OyXRv3MSretVdyniKrBtvp0Tc83X/lvxVqkAoomgUqFu2Xjb7SbfowtSZKyt1KfJoFJVZ/HHMOVvkHQqDJngf60dhYWwbjrMeRm2z4P4unDW7XD6cIhJPPr9Ze2CMZfBnrVw5QfQ9pLKjzlA7cnK5ep35rIzM5ebe7Zg/PztpVYNzcjO59uVO/hySSpzN6ZhDLRtmED/Tkn069SQxjWDdN7FnatsS7W/VbxVKsBoIqhUqHulPWRsO3J5YhM7Vkgp5Xu/vAbfP2YLtAwcY8dF+StjYMsvNiHcMBOiE2wyeNbtdpyhN9K3wZhLbVXUQWPt61YApGfnMejd39icdoCPbjyTM1p4d0FgV+ZBpi3bwZSlqSzZlg5AcrOa9O+cxEUdGlKnWhAWWvGnirdKBSBNBJUKdU/UAEr7bgs8kV7V0SgVWoyBGY/bRPDky+GydwJr4vTUJbbL6KovbUXHU6613UZrNiv7OWkb4KMBcDDDtnw2Pavq4vVz+w/mc+3I31m9Yz+jrk+mR5u6x7SfrWnZTF2WypQlqazZuZ/wMKFrq9r075TE+e0bkBATWcmRu6y8irfLJ+jQB6XK4DeJoIhcALwGhAMjjTHPlVh/K/B/QAGQBYwwxqxy1j0MDHfW3WmMmV7esTQRVMqxZS58eDGYgiPXaYugUr5VcAim3WW7hCYPh4teDNz50tI22HL/S8aBKYQOV9rCMvXaFh+DXK0e5GXbZHfoF9Cwk9uR+43svENc//58Fm3dx1vXnkbfdvUrZb9r/tzPlKUpTFmayra9OURFhNH7xLr079SIc9vWIyYyQD9zpTmYAfNHwW//gwO7oWYLyEyBgrzD2+jQB6X+4heJoIiEA2uBvsB2YD4wuCjRc7ZJMMZkOj/3B243xlwgIu2AccAZQBIwAzjBmNLObC1NBFXIKyy0J20//MtWYMvbb7vVFAmLsIUb9B+lUr6RfxAmDoc/psHZD0Kvh4NjuoTMVJj7Jiz4wE590aAT7P4DCjwrXwr0fdLODacAOJhfwM0fLeCX9Xt4bdAp9OuUVOnHMMawZFs6U5amMm3ZDnbvzyU+KpzzTm5A/85JdG9dh8jwIJkpLD/HXmD55sHSL3RWqw8jZttxuDo3oQph/pIIdgGeMMac7zx+GMAY82wZ2w8GhhljLiy5rYhMd/Y1t6zjaSKoQlrWbjtp84aZcPJl0O81WDv98BX7yDh7Ajfgf3DKELejVSr4HMyET6+xpfAvfMGWwQ822Xth3rsw+3nbQliS9jj4S35BIbd9vJAZq3fx4pUduSq5ic+PWVBo+H1jGl8uSeWbFTvIPHiImnGRXNShIf07JXF681qEhQXBhYkyhz54iIy3CWFcLYgt776mc18boqtX3oUbrdqtXHQ0iWAlThp0hEaAZ7WK7cCZJTcSkf8D7gWigKKR5Y2A30o894g6wiIyAhgB0LRp00oJWqmAs/ln+Hy4Ldl+8cuQfKP9Z9Zx4OF/PAX5MPZKmHqXLRPfooe7MSsVTLJ2w9grYOdKuPy94D3hi6sFvR6CH58rfX3G9qqNx08VFBru/mwJM1bv4ukBJ1dJEgjYMYOt69C1dR2euvRkflq7hylLU5m0KIWxv2+lYaIzR2GnRrRvlBC4cxQmNi69GFpcbej9COTshex9zv1ee5++xf58MIMyk8iwiFKSxZqlJI+1D/8cW/PI+TdLVu3O2GYfQ/D+bVABy5eJoFeMMW8Cb4rINcA/geuO4rnvAu+CbRH0TYTKNXpFrXyFBTDnP/Djs3bMxJAJ0LBj6duGR8JVo+H98+Gza+GmGe5NaK1UMNm3xU6XkJkKgz+FNn3djsj3yjoRT2xc9bH4mcJCw98/X8ZXy3bwj4tOYmiX5q7EER0RTt929enbrj7ZeYf4ftVOpi5N5cNfN/PenE20rBPPJZ2S6N8pidb1/LiabWnOfaz06ZEueK7ic4TCAshJL54ket5npx1OJPduPLzOczxiSdGJHi2LtWDLr8VjA/t45lN6DqP8ji8TwRTA8zJYY2dZWT4F3jrG56pgo1fUype1CybeBJtmQ4er4JJXbLeW8sTWgGs+g/fOtZM93zQT4mtXTbxKBaNdq20SmJ8Nw76Epkd0eglOZZ2In/uYezH5AWMMj01ZwcRF27m7TxtG9GzldkgAxEVFMKBzIwZ0bkR6dh7frPiTKUtS+e8P63h95jpOTiqaozCJpBqxAExenMKL09eUOs+h64rOAY7lQnFYuP2/dzT/+4yBvAOlJI1lJJL52aXvJ2MbzHoWmpwOjZLt/2SlXObLMYIR2GIx52KTuPnANcaYlR7btDHGrHN+7gc8boxJFpGTgU84XCxmJtBGi8WEEJ0Hr2wbf4SJN9ty2he+AKcOO7pxDdvmw+hLoGFne/IaGeOzUAOatkir8mybZy+oRMTA0ElQ/2S3I6pa+v0oxhjDs9/8wbs/beSWni156MKT/L7r5U6POQqXOnMUnt68Js1qxTFt+Q4O5h8eBxobGc6zl3fwn2TQn5V1/hIWaYvcmEJAoO6J0OQMaHyGva/dBsKCpLCPcpVfFItxArkIeBU7fcT7xph/i8hTwAJjzBQReQ3oA+QD+4A7ihJFEXkEuBE4BNxtjPmmvGNpIhgk8g/Cmq/g8xvL2CCE58ErLLBFGma/YLt1XvXhsZ98rvwCJlwP7a+EK0YGR2XDylSyRRq0PLk6bN0MGD8Uqjew0yXUbO52RMplr3y/ltdmrmNYl2Y82f9kv08CS9qSdoCpS1OZsjSVtTuzSt2mUY1YfnnonFLXKQ/l/f848UJIWWgvJG2bB9vnw0HnnCamBjQ+3UkOT4fGyRX39FGqFH6TCFYlTQQDmDH2j+GST2DlJDuYW8JLLw8dWxPuX3/k4Oxgl7kDJt1sKxJ2ugYufgmi4o9vn3P+Y6/o9/w7nPNI5cQZLMq6oqvlydXyz22F3npt4dpJdg49FdLemb2BZ7/5g6tOa8zzV3QM+MqcLR76qtRyKgJseu7iqg4nMHnbYl5YCGnrYdvvsH2e7bGze7VdJ2FQr52THJ5pE8RaLfXCraqQv1QNVap8Gdth6aewdJz9QxgRC+36Q6fBkLUTpt1d/IqahNnKmP87y85XdeJFofEHcf1MmDTCjjuozOkfut9rB8P/9IL959J5cOXsNxiUVf0waye8fJL9+WjKk8fVtj9refLANu89+PoBaNYVBo+DmES3I1Iu+2juZp795g/6dUriuSBIAgGSasSSkp5T6nLlJc+q3eUJC4O6J9jbqUPtspx0SFlgk8Lt82DFRFj4gV0XV9uj1fAMaHTq8V8UViFNE0FVtfKy7WTLS8bCxtmAgaZdodvd0G4AxCQc3lbCjjzRjaoGMx6383U17QJ9n7YDr4NRwSGY9W/4+WWo29Z2Ba13UuXtXwQufsVWPZzyN6jRBJp3r7z9B6qCQ4fnXSwprjb0/seRpcmzPcuTl9N1ubzy5EXJYslEUsuTu88YO2XC7OfsBagr37ddvVRIGz9/G499uZK+7erz8sBOhAdBEgjwwPkn8vCk5eTkH+6VEx0RxgPnn+hiVCEktga07mNvYIeF7F4e2ZSNAAAgAElEQVRzuMVw2++w9lu7TsKhQfvD4wwbn267qofCRXJVKbRrqPI9Y2DrXKfr52TI22/nsus0GDoNsq1RR6PgECweA7OegQO7bAJ57uNQ2z8qtFWKjBSYONy+b6cMtUVhouJ8c6ycfTDqPFuJ9KaZUKe1b44TCPIP2vGpa76ySVvhocPrvB0jWHDIJoOlVZQrdl8imfS6PHlt2PJL6ZXp4urAoLH2gklUvG2BjKpmu7FW9YlBsLRYFhbCN3+H+e9B5yH2MxBqXdPVEaYsTeWuTxfTvXUdRl6XTHREuNshVSrPqqEGOKtFLT69pYvbYaki2Xth+wInOfwdUhZBnjO2M77e4aSwyZmQ1PnIC1fB8vdZlUrHCCr/sG/L4a6f+zbZrnQnX2oTwGbdjr86Vm4WzH0TfnkNCnLtROo9/w7V6lZO/G5Z+50dg3QoF/q9WjV/nPdugpF9bIvs8BmhOa3EwQwYdw1s+dkm3rE1q+4fpTH2n3ixZHFf2Ulk6uKj239YhE0Mo6pDtJMkRlVzEsWin6vZ9VHxzs/VPJZXK74sMrb8xDJYiu0cyoPJt8GKz6Hr32wPBL3SHvK+W/knt41dxGnNajL6hjOIjQquJLCkJ6asZMxvW5h1Xy+a1vbRBUl1fAoLYNcqmxQWdSndu9GuC4uABh1tctjkDHvRd+aTgf/3WZVJE0HlntwsWD3Ftv5tnmOXtehpC5y07WdPJivb/p22mubCD+0fs253Q5fbA6/ffEG+TTx+fR3qt7ddQaty0vdt8+DDS+yYg2FfhlYxlKxd8PEV9h/ppW9Dx6vcjqh8ZRWzia8Hl71tk8q8A/b7mLff4+csyHUe52Udub4g17vjS9iRiaJnC+Sar+w+Swqk6V/yDsBnQ2HDTOjzJHS/2+2IlB+YvXY3N49eQLukBD6+6UyqRQd/6/DOzIP0fGEWAzon8cKVndwOR3kra7ctxFfUpTRlIRw6cuznXwLp77MqlyaC/iCUmt0LC20rypJxsOpLO7aqZgvbjarT1bYbaFXYvdZe5fpjGlRrYMdydR4SGN240rfZLonb58FpN8AFz7ozBmnFRBtHx6vhsndCo/Vj3xYYc6mtzHr1GGjT1+2IKuarFreCfI8E0ePeq589Es99m8s+xkmX2GlP6rWzt1ot/e87mr0XPhloT5z6vWbn6lQh7/eNaVz3wTxa1qnGuJvPIjEu0u2QqsyTU1fy0dwt/HDf2TSrHWAXWZVVkA87V8C7vcrYIISn5woymgi6LVi6RVUkbYPT9fNTyNgK0Qm262fnIbZfultJxJa58P2j9kpY3ba2wmib8/w3qfnja9v9rLAA+r8G7a9wN56fXoQf/gW9HoZeD7kbi6/tXAUfX27H210zAZqe6XZE3vPni01ltVhGxNpY925wJlUGwqNtxbx6J9spGeo79wmN3PnOZqbCmMttjFe+b3syqJC3eOs+rh35Ow1rxPLZiLOoXS2EekwAuzIP0uOFWfTrlMRLV2mrYEAr6++ztggGDU0E3VbWl6x6Q7hnJYQF8HiCg5l2MvKl42whEwRa9bZdP0+62HcFTY6WMbaL6ownbD/55j1sQtjoNLcjO+xQno3vtzdt//2rPvSPgjfGwOTbYekncNm7tlU3GG2bB2OvgogYOyl4/XZuRxQ8KroYlp9jq+DtWg27Vtr7natgf+rh7aMTncSw3eHWw/rt7NhNX9mzHsZcZsdnDv7EdmtXIW9lagaD3/2NmvFRjL+lC/UTYtwOyRVPTV3F6LmbmXnv2TSvo62CAau0v88A5z8DXf7PnZhUpdJE0G1P1IBSp2PFDtpNaGS7S9Zo5twX3ZpA9ST/6yZVWACbZttxf6un2T7mdU6wRV86Xg2JjdyOsGwF+Xbs4I/PQfYeOPlyOPfRo69UWtn2bbZdMFMWwhkjbBGKSD86uTiUZ1vKtv1uxws26+p2RJVr3QwYPxSqN7BJYM3mbkcUfI6lxTJnn5McrrKJ4S7ndjDj8DbVGzqJoUfrYd2Tjr8rdeoSO04U4NrPIemU49ufCgrrdu7n6nd/IyYijPG3dqFxTT+52OmCXfvtWMGLOyTxn4HaKhjQPP8+V29g5y6s1RJu+j7w6iuoI2gi6LayWgRja9rxXxnbIH2rve3fUXwbCbeJVVGSmNikeLKY0KjqEsU962zyt+wzyEyxkye3vxI6X2Nb1vy1q2VpDmbaIiy/vmGnBDj9Juj5gDvVMVdNgS/vsD8P+K+d/sIfZe+FUX0hO81OK+EPrZWVYfnntiprvXZw7USoVs/tiFR5jLHdNYu1Hq60LYpFxW0kzJ7E1GtbvItpzRZl/730PBGKr2P/RlSrB0Mnh/YUKuovm/ccYOA7czHA+Fu60EJbwfjXtFW8/8smZt7XS9+PYLJ+hu0h07a/7Z0USOd36giaCLrtaMYI5h+0SVb6FlswpChBLJYoevyOJBwSkkq0JDY9nDQmNoZwLwewl3bFvk1fWDHJdv3cPt8er/W5Nvk74UL/arU6Fpk74MdnYPHHtrph93vgrNuqpjDLoVz47p8w711IOtWOP6rVwvfHPR57NzrTStSAm2bYSc4D2e/v2jnhmnWzXf9iEt2OSB2rwgL7+SzZerh3Y4nxhyce7lZalCRu+aWUrlECFzwPZ93iystR/iUlPYeBb88lO+8Qn93ShRPqV3c7JL+we38uPV74gYvaN+Tlqzu7HY6qTL+8Bt8/Zudl7nGv29Go46CJoD+orEIOh/Igc/uRCWJR0piZQvFEMcx2L/XsblqsRbExRESVnqyKM3bRFNgTp87XQIeBUL3+cb0VfmnXajs+b+23tpW19yN2cntfjd/cuxEm3AA7lsBZt9ty9BFRvjlWZdv6G4zuZyenHfpFYE4rYYztHjz7OTjxYpuEB/pFDVW6v8YfrvJIElcXH3+IUGr3fS2WoLCFUQa+M5e0A3mMu/ks2jfSC0ae/v3VKkb9vIkZ955Ny7o+mBJKucMYmDjcNgYMmRAYFbRVqTQRDCWH8pwWRSdBzCjRqpiZcvjqOABix9hk74GCvCP3F1UNrv8KGnYKja4Bm+bYCqOpi21rQd+nbAtoZb72FZNgyp0QFgaXvmWL6gSa5Z/bfxAdB9l56gLps1FYCN88APNHQudr7XQA/jYOV/le9l7Y/YftVvr1/WVspOXTQ11aVi6D3v2NlPQcxgw/k9Oa+bA4UYDak5VL9+d/4ML2DXlFWwWDS142vH8e7NsKI2YFz5CQEKOJoDqsIN9JFEskiEs/KeMJIXgiVFgIq76AGU/aLrotzrYJYdJx/oPLPwjTH4YF79vWtCvfr7o5FX1h9gsw69+29fTsv7sdjXcO5cHkW+38iF3vtL/XQEpilW9o+XRVioycfK557zfW78riwxvOoEsrF8aQB4hnvl7NyDkb+f7es2mlrYLBZd8WO9dgfF24eSZEa7foQHM0iWCYr4NRLguPtBURW/SAU4ZA74fhsrfsCU9pEhtXaXh+ISzMzt13x3y44Dn4czm8ezZMvNn+QTwWe9bbsXUL3rcJyA3fBHYSCLa4TqfBNhlc/rnb0VQs7wCMG2STwL5PwXlPaxKorHMfO3JccGSsXa5CUlbuIa7/YB5rd+7nnaGnaRJYgRE9WxIdEc7rM9e5HYqqbDWb2YIxaevhi1vtxXIVtDQRDFV6InSkiGhbOObOxbaIzOop8EYyTH/Edivz1rIJNpHMTIFrxtsExNsCPv5MxHarbNYNJt9mxw76q+y9MLo/bJwF/d+Abne5HZHyJx0H2uJdiU0AsfelFfNSIeFgfgE3jZ7Psu0ZvHHNqfQ6USsJV6ROtWiGdW3GlKWprN+V5XY4qrK1PBvO/zf8MQ1+etHtaJQPadfQUFZZBW2CVcZ2mPWMnUIjJgF63G/n/CuryEheNnz7ICz6CJqcZbuC+vMci8cqe69t7czZZyuJ+tsYgsxUOyn43k32d9D2ErcjUkr5qdxDBYz4aCE/rdvNq1d3ZkDnIPyb7SNpWbn0eGEWfdrW5/XBOu9m0DHGXvRdOg4GjYOTLnI7IuUlHSOoVGX6cwXMeNzOs5PYBM55FDpcBSs+P5xIV6tvK7buT4Xu99pxdMFckCRtA4w8F+Jqw/Dv/WdaiT3rYcyldnLcweNsl2illCpFfkEhd3yyiOkrd/L8FR24+vQA777vgue++YN3ftrAd3f3pI1OsRF88nPggwvt/9abZ9rpeJTf0zGCSlWmBu3txONDJ0NsTfhiBLza0U4Kn7ENMJD1p00Cu90FfR4P7iQQbCvgoE9s4aHxw2xRFrelLob3z7f/uK6fpkmgUqpMBYWG+8YvZfrKnTzRr50mgcdoRM+WxEWG8/oP690ORflCZCxc/bHtCTVusL3IqoKKJoJKeatVbxgxGy5/zyZ9BblHbrNiUtXH5ZZmXWHAm7B5Dky9y3Yjccumn+DDfhAZBzdOP/6Kr0qpoFVYaPjHpOVMWZrKgxecxPXdWrgdUsCqFR/FdV2bM21ZKmt37nc7HOULiY1h4Ee2qvqkm6GwwO2IVCXSRFCpoxEWZsdRmjKqaGVsr9p43NZxIPR62E5HMucld2JYPRU+vsL+sxo+Heq0dicOpZTfM8bw1LRVfLZgG3ee05rbevnZGOcAdHMP2yr4mlYQDV7NusKFz8O672ztBBU0NBFU6liUNc1GKE6/cfaD0PFq+OFfdqqGqrToI9s1tWEnuOFrSEiq2uMrpQKGMYYXpq/hw183c1P3FtzT9wS3QwoKNeOjuL5bc75evoM1f2qrYNBKHg6nDrMXfVdOdjsaVUkqTARFJLwqAlEqoOj0G4eJQP//QtOu8MVtsPX3qjnuz6/ClL9Bq3Ng2Jf+U7BGKeWX3vhhPW/9uIEhZzblkYvbIjqvaKW5uUdL4qMidF7BYCYCF70EjU+HybfDzpVuR6QqgTctgutE5EURaefzaJQKFDoPWXER0TBorJ0u49PBduoGXzEGvvunreTa/kpb1joq3nfHU0oFvJFzNvKf79dy+amNeHpAe00CK1mNuChu6Nacr5bv4I8/M90OR/lKRDQMHAPR1eHTa45ujmXllyqcPkJEqgODgBuwieP7wKfGGL/6puv0EUr5gT3rYVQfiK8Lw7+zVVYrU8EhmHonLBlr53S84Hk7blMppTxMXpzCi9PXkJqeQ0JsJBk5+VzcoSGvDepMRLj+zfCF9Ow8ejw/i+5t6vDWtae5HY7ypW3z4cOLoHl3GPI5hGnnQX9SqdNHGGP2G2PeM8Z0BR4EHgd2iMhoEdGqDEqpw+q0hqvH2hbBz4ZW7rQS+TkwfqhNAns9DBe+oEmgUuoIkxen8PCk5aSk52CAjJx8wgR6n1hXk0AfKmoV/GbFn6xK9au2AlXZmpxuu4lu+AFmPul2NOo4eDVGUET6i8gXwKvAf4CWwFTgax/Hp5QKNM272TGDm+fAtHsqZ1qJgxnw8ZWw5hv7z6fXQ3a8glJKlfDi9DXk5BcvcV9o4JUZOn7N14Z3b0n1GB0rGBJOuw5Ovwl+eQ2Wf+52NOoYeTPr9TpgFvCiMeZXj+Wfi0hP34SllAponQfDvk0w+3mo3RJ63Hfs+8raBR9fDrtWwxUjocOVlRenUiropKbnHNVyVXkS4yK5sVsLXpu5jpWpGZyclOh2SMqXzn8Wdq6CL++AOidAw45uR6SOkjd9JDoaY4aXSAIBMMbc6YOYlFLBoNfD0OEqmPkUrJh0bPvYtxnePx/SNsDgzzQJVEpVqF5CdKnLk2rElrpcVa4bu7egekwEr2kLbPCLiIKBo23V7k+HwIE0tyNSR8mbRPBNEalR9EBEaorI+z6MSSkVDESg/xvQ5Cz44lY7uPxo7FwFo863VcmGfQlt+vgmTqVU0DiQe4jwUrqNx0aG88D5J7oQUehJjI1kePcWfLdqJytSMtwOR/latXpw9ceQtRMmXGeLuqmA4W2LYHrRA2PMPuAU34WklAoakTEw6BM70fu4QbaFzxtbf4cPLrDJ5I3fQpMzfBqmUirwFRYa7vlsCX9mHmREzxY0qhGLAI1qxPLs5R249JRGbocYMm7s3oKEmAhe07GCoaHRqdDvNVsb4PtH3Y5GHQVvxgiGiUhNJwFERGp5+TyllIL42jBkAozsA2MHOtNK1Ch7+7XfwfhhNnkc+gXUbFZ1sSqlAtaL363hu1U7ebxfO27o1oJ/XKTTH7slISaSm3q05OXv17IiJYP2jXSsYNDrPBj+XAa//Q8adLSPld/zpkXwP8BcEXlaRP4F/Aq84NuwlFJBpU4b23Vk70ab5BXkl77dsgl2Qvq6J8CN0zUJVEp55fOF23nrxw0MObMp13dt7nY4Cri+W3MSYiJ4dcZat0NRVaXv09CiJ0y9C1IWuR2N8oI38wh+BFwB7AT+BC43xozxdWBKqSDTogf0fx02zS59Wonf34FJN0HTLnDdNKhW1504lVIBZf7mvTw8aRndWtfmif4nIzq1jF9IiInk5h4tmbF6F8u361jBkBAeAVd+CNXqw2fX2qrfyq+J8XKOLxGpB8QUPTbGbPVVUMciOTnZLFiwwO0wlFIVmfk0zHkJTr4cts+HjO0QXR1yM+GkS+CKUXZsoVJKVWDb3mwGvPkLNWIj+eL2biTGRbodkvKw/2A+3Z+fRXKzmoy6/nS3w1FVZccyGHUeJHWGYVNsdVFVZURkoTEm2ZttvZlQvr+IrAM2AbOBzcA3xxWhUip09X4EGiXDykmQsQ0wNgmUcDipnyaBSimv7D+Yz/DR8ykoNIy8LlmTQD9UPSaSm3u0YOYfu1i6Lb3iJ6jg0LAjDHgDts6F6Q+7HY0qhzdjBJ8GzgLWGmNaAOcCv/k0KqVU8AoLg/1/HrncFMCsf1V9PEqpgFNQaLhz3GI27j7AW0NOpWXdam6HpMpwXdfm1IiL1AqioabDldD1Tpg/EhaOdjsaVQZvEsF8Y0watnpomDFmFuBVc6NSSpUqM6X05RnbqzYOpVRAeubr1cxas5snB5xM19Z13A5HlaO6M1bwhz92sURbBUNLnyeg1Tnw9f2wbZ7b0ahSeJMIpotINeAnYKyIvAYc8G1YSqmgltj46JYrpZRj3LytjPp5Ezd0a86QM7WycCC4rmtzasZFagXRUBMWbsf9JyTBZ0Mhc4fbEakSvEkEBwDZwD3At8AGoJ8vg1JKBblzH4PI2OLLImPtcqWUKsOvG/bw6OQVnH1CXR65qK3b4SgvVYuO4OaeLflxzW4Wbd3ndjiqKsXVgkGfQO5+GD8UDuW6HZHyUG4iKCLhwDRjTKEx5pAxZrQx5nWnq6hSSh2bjgOh3+uQ2AQQe9/vdbtcKaVKsWnPAW77eBEt6sTz32tOISLcm2vZyl9c16U5teKjeG2GjhUMOfVPhsvespXCv7rvyOmjlGvK/StqjCkACkUk8Vh2LiIXiMgaEVkvIg+Vsv5eEVklIstEZKaINPNYVyAiS5zblGM5vlLKj3UcCPesgCfS7b0mgUqpMmRk5zP8w/mEhwmjrjudhBitEBpo4qMjGNGzJbPX7mbhFm0VDDntBkCP+2HxGFgwyu1olMOby2lZwHIRGSUirxfdKnqS05r4JnAh0A4YLCLtSmy2GEg2xnQEPgde8FiXY4zp7Nz6e/VqlFJKKRVU8gsKuf2ThWzbl83b155G09pxboekjtHQs5pRKz5KxwqGqt7/gDbnwzcPwpZf3Y5G4V0iOAl4FFssZqHHrSJnAOuNMRuNMXnAp9jxhn8xxswyxmQ7D38DtFKEUkoppQAwxvDk1JX8sj6NZy7rwBktarkdkjoO8dER3NKzJXPW7WHhlr1uh6OqWlg4XP4u1GwO44dppXA/UGEi6IwLPOLmxb4bAds8Hm93lpVlOMUnqo8RkQUi8puIXFraE0RkhLPNgt27d3sRklJKKaUCxUdzt/Dxb1u55eyWXJXcxO1wVCUY2qUZdapF8aqOFQxNsTVs8Zj8g/DpEMjPcTuikFZhIigim0RkY8lbZQYhItdi5yZ80WNxM2NMMnAN8KqItCr5PGPMu8aYZGNMct26dSszJKWUUkq5aPba3Tw5dSV929XnwfNPcjscVUnioiK4pWcr5qzbw4LN2ioYkuqeaFsGdyyBafdo8RgXedM1NBk43bn1AF4HPvbieSmA5+W7xs6yYkSkD/AI0N8Y81dNWWNMinO/EfgROMWLYyqllFIqwK3ftZ87xi7ixAYJvHp1Z8LCxO2QVCUaclZT6lSL4hUdKxi6TroIev0Dlo6D3992O5qQ5U3X0DSPW4ox5lXgYi/2PR9oIyItRCQKGAQUq/4pIqcA72CTwF0ey2uKSLTzcx2gG7DK61ellFJKqYC090AeN364gOjIcEZel0x8dITbIalKFhcVwa1nt+KX9WnM26StgiGr5wNw0iUw/RHYONvtaEKSN11DT/W4JYvIrUCFf5WNMYeAO4DpwGpgvDFmpYg8JSJFVUBfBKoBE0pME9EWWCAiS4FZwHPGGE0ElVJKqSCWd6iQWz9eyJ+ZB3l32Gk0qhHrdkjKR4ac2Yw61aK1gmgoCwuDy96GOm1gwvWwb4vbEYUcMRX0yxWRWR4PDwGbgP8YY9b4MrCjlZycbBYsWOB2GEoppZQ6BsYYHpy4jPELtvPaoM4M6FxefTkVDEb9vImnp63isxFncWbL2m6Ho9yStgHe7Q01msLw7yBKp4g5HiKy0KmzUiFvuob29rj1NcaM8LckUCmllFKB7b05Gxm/YDt3ntNak8AQMeTMptStHq1jBUNd7VZw5SjYuQKm3KHFY6qQN11DnxGRGh6Pa4rIv3wbllJKKaVCxYxVO3n2mz+4uEND7u5zgtvhqCoSExnObWe34reNe5m7Ic3tcJSb2vSFcx+DFRPh19fdjiZkeFM19EJjTHrRA2PMPuAi34WklFJKqVCxekcmd326mA6NEnnpqk5aITTEXHNmU+pV17GCCuh+D7S7FL5/DF5sBU/UgFfaw7LxbkcWtLxJBMOLKngCiEgsEF3O9koppZRSFdq9P5ebRi+gekwk7w1LJjYq3O2QVBWLiQzntl6t+H3TXn7dsMftcJSbRKB1H0DgwB7AQMY2mHqnJoM+4k0iOBaYKSLDRWQ48D0w2rdhKaWUUiqYHcwv4JYxC0g7kMvI65KpnxDjdkjKJYPPaEr9hGhenbGOiooYqiA3+3mgxGcgP8dOMZG1W8cPVjJvpoF43pnGoY+z6GljzHTfhqWUUkqpYGWM4aGJy1i0NZ23rz2V9o0S3Q5JuSgmMpzbe7Xm8Skrmbshja6t67gdknJLxvbSlx/YBS+1hphEqN3a49YKarex91HxVRtrEKgwERSRFsCPxphvncexItLcGLPZ18EppZRSKvi8OWs9k5ek8sD5J3JB+4Zuh6P8wNWnN+GtHzfwyoy1dGlVGxEdKxqSEhvb7qAlxdWBnvdD2np72/wLLPus+DbVk5zEsLWdm7AoWazRFMIjqyb+AFNhIghMALp6PC5wlp3uk4iUUkopFbS+Wb6Dl75by2WnNOL2Xq3cDkf5iZjIcG7v3YrHvlzJL+vT6N5GWwVD0rmP2TGB+TmHl0XGwgXPQseBxbfNy4a9Gw8nh2kbIG0drPwCDqYf3i4sAmo2P9xy6NmiWL2BHZsYorxJBCOMMXlFD4wxeSIS5cOYlFJKKRWElm/P4J7xSzi1aQ2evbyDtvqoYopaBV+dsZZurbVVMCQVJXszn7LdRBMb2+SwZBIIduL5Bu3traTsvR4J4nrYs84mihtnwaGDHvuodmRyWNTlNKaMLuvLxnsXXwDwJhHcLSL9jTFTAERkAKBlnZRSSinltZ2ZB7npo/nUjo/mnaHJxERqhVBVXHREOLf3bs2jk1fw8/o99GhT1+2QlBs6Djz+xCquFsSdAU3OKL68sBAyU4oniWnrIWWhbUk0hYe3ja93OCks6mqath5+fPZwi2VRVdOiuAOMVFSdSURaYSuHJgECbAOGGWPW+z487yUnJ5sFCxa4HYZSSimlSsjJK2DgO3PZuDuLz2/rStuGCW6HpPxU7qECer/4Iw0SY5h4W1dtFVRV51Au7NvstB56djddb4vVlCexCdyzokrCrIiILDTGJHuzrTdVQzcAZ4lINedx1nHGp5RSSqkQUVhouG/CElakZvDe0GRNAlW5iloF/zl5BT+t28PZJ2iroKoiEdFQ90R7K+lghk0I3zun9OeWVe3Uz3kzjyAicjFwO3CviDwmIo/5NiyllFJKBYNXZ6zl6+V/8o8L29KnXX23w1EBYGByExrViOXVGWt1XkHlH2ISodFptuWvNImNqzaeSlJhIigibwNXA3/Ddg29Cmjm47iUUkopFeC+XJLC6z+s5+rkJtzUo4Xb4agAERURxv/1bs3irenMXrvb7XCUOuzcx2wVU0+RsXZ5APKmRbCrMWYYsM8Y8yTQBTjBt2EppZRSKpAt2rqPBz5fxpktavH0pe11rJc6Klee1phGNWJ5ZcY6bRVU/qPjQOj3utMyKPa+3+sBWSgGvKsaWjSRR7aIJAFpgM7+qpRSSqlSpaTnMOKjhTRMjOHta08jKsKrkShK/SUqIow7zmnNw5OW8+Pa3fQ+sZ7bISllVUZVUz/hzV/maSJSA3gRWARsBj7xZVBKKaWUCkwHcg8x/MP55B4qYNR1ydSM16mH1bG58rTGNK4Zy6vf61hBpXyhwkTQGPO0MSbdGDMROzbwJGNMYHaEVUoppZTPFBQa7vp0Cet2ZfHmNafSul51t0NSASwyPIy/ndOapdszmLWmgvL9SqmjdlR9NYwxucaYDF8Fo5RSSimYvDiFbs/9QIuHvqLbcz8weXGK2yF55YXpfzBj9U4eu6QdPbXsv6oEl5/amCa1YnlVxwoqVem0075SSinlRyYvTuHhSctJSc/BYMfbPTxpud8ngxMWbOOd2RsZelYzruva3O1wVJCIDA/jb73bsGx7Bj/8oa2CSrmPMxgAACAASURBVFUmTQSVUkopP/Li9DXk5BcUW5aTX8DjU1by7Yo/WbR1H9v3ZZN7qKCMPVS9eZv28o8vltO9dR0e69fO7XBUkLns1EY0rRWnrYJKVbIyq4aKyKnlPdEYs6jyw1FKKaVCU2GhYfa63aSk55S6PiMnn1s/XlhsWc24SOonxFC3ejT1E2Ko53FfLyGaetVjqJcQTXREuM/i3pqWzS1jFtCkVhxvXnMqkeF6jVlVrshwW0H0758vY8bqXfRtV9/tkJQKCuVNH/GfctYZ4JxKjkUppZQKOQfzC5i8OIWRP29i/a4swgQKS2n0aJAQw8jrktm9P5edmQfZ5XG/K/Mg63dlsXt/LodKeXKNuMi/kkTPpLFe9RjqeySMMZHeJYyTF6fw4vQ1pKbnEB4mRIYLo647ncS4yON9O5Qq1eWnNOLNWet5dcZa+rStp/NSKlUJykwEjTG9qzIQpZRSKpSkZeUy5rctjJm7hbQDebRrmMDLAztRWGh49MuVxbqHxkaG89CFJ9G+UWK5+ywsNOzNzmNXZi479x9kd6ZHsrj/IDszc9m4+wC79h8kv+DIhDEhJsImiQnR1K8eQ92E4sli/YRoft+4l8enHI7vUKEhLExYui2dFnXiK/dNUsoRER7G385pw/0TlvL9qp2cd3IDt0NSKuB5M6E8ItIeaAfEFC0zxnzkq6CUUkqpYLV+135G/byJiYtSyDtUyDkn1eOm7i3o0qr2X60cEeFhf7W4JdWI5YHzT+TSUxpVuO+wMKFOtWjqVIumHQllbldYaEjPyS/WsvhXS2OmTRp/37SX3ftzySsorPC4eYcKeXH6Gq9iVOpYXdo5iTd+WMerM9bRt119bRVU6jhVmAiKyONAL2wi+DVwIfAzoImgUkop5QVjDHM3pPHenI3MWrOb6Igwrji1McO7Ny91rr1LT2nk06QqLEyoFR9Frfgo2jYsP+707Pxi3VDvn7C01G1TyxjbqFRlKWoVvG/CUqav3MkF7bVVUKnj4U2L4JVAJ2CxMeYGEakPfOzbsJRSSqnAl3eokGnLUhk5ZxOrdmRSOz6Ke/qcwLVnNaV2tWi3w6uQiFAzPoqa8VGc2MAmrK98v7bUgjZJNWKrOjwVggZ0TuLZb1ZzxyeLKCg0R9VirpQqzptEMMcYUygih0QkAdgFNPFxXEoppVTAysjOZ+y8LYz+dTM7M3NpXa/a/7N33/FV1fcfx1+f7DDDRiBCQBvZoIAscVZwIVWqtmhVtK7i+LXVap21tVLR2rq32Kp1AEXAAXUAIihbEAEFw0hAdpgJZHx/f5wTCCHjhoxzb/J+Ph73kXvP+J7PufkG7ud+F6Mv6sqwnq1DnpAlXN0+OJW7Jiw9Ygzj7YNTA4xKaospSzayMyvn4KRIBetsAkoGRcoplERwvpklAS8CC4A9wJwqjUpERCQCrd22l1dmpfHO/HSycvIYeFxTRl/cjVOPb0ZUVM0Yz1TwYftoxjCKVNSYqSuPmOgoKydPY1RFjkKZiaBz7ib/6XNm9hHQwDm3pGrDEhERiQzOORas3cGLn//AtG83ERNlDO3emmsGptCpVckTtkSyqh7DKFKSksaiaoyqSPmFMlnMJOAt4D3n3Joqj0hERCQC5Obl89GyH3np8zQWr8+kYWIsN53WgV/1a0eLBgllFyAi5dYqKVFjVEUqSShdQx8DLgUeNrN5eEnhFOdcdpVGJiIiEoZ2Z+fw9rz1vPrFGjIys2jXpA5/vrAzF5/UhjpxIa3KJCJHqbgxqjFRpjGqIkchlK6hM4AZZhYNnAH8GngFSlmgSEREpIbJyMxi7BdpvDV3Pbv359KnXWPuv6ATZ3ZsQXQNGf8nEu6KjlGNj40iL8/Rr0OTgCMTiTyhLiifCFyA1zJ4IvBaVQYlIiISLpakZ/LS52m8v3QjAOd2PYZrB6bQPTkp4MhEaqfCY1TXbtvLWX+fwWPTVvLI8O4BRyYSWUIZI/gO0Af4CHgKmOGcy6/qwERERIKSn+/4ePkmXpqVxty07dSLj2HkgHZc2b8dbRrVCTo8EfG1bVKXK/u14+Uv0riqf82doEmkKoTSIvgy8AvnXF6ZR4qIiESwrAN5jFuwnle+WEPa1r20TkrknvM6cmnvZOonxAYdnogU4+YzjmfcwnT++sFy/n1NH8zUVVskFKGMEZxaHYGIiIhUl4mLMg5bB+/6QSls3n2A179aS+a+HLonJ/HUL3sypHNLYqKjgg5XRErRsE4st5xxPA9O+ZbpK7dw+gnNgw5JJCKYc67soyJAr1693Pz584MOQ0REwtzERRlHzDpYYHDnFvz6lPac1LaRWhVEIsiB3HzOfnwGMdFRfHTrKfoCR2otM1vgnOsVyrH6KxERkVplzNSVxSaBLRrE8/wVvejVrrGSQJEIExcTxZ3ndGTV5j28NW990OGIRIQyE0Ez+ySUbSIiIpFgQzGLUQNs3rW/miMRkco0uHML+rRrzOP/+47d2TlBhyMS9kpMBM0swcwaA03NrJGZNfYf7YDWoRRuZkPMbKWZrTKzO4vZ/1sz+9bMlpjZJ2bWttC+K83se/9xZflvTURE5EitkhLLtV1EIoOZcc/5Hdm29wDPTl8ddDgiYa+0FsHrgQXACf7Pgsd7eMtIlMpfgP5p4BygE/ALM+tU5LBFQC/nXDdgHPCIf25j4H7gZLylK+43s0ah35aIiEjxRg5od8S2xNhobh+cWv3BiEil6tYmiWE9WvHyrDQySmj9FxFPiYmgc+6fzrkU4PfOufbOuRT/0d05V2YiiJfArXLO/eCcOwC8BVxY5BqfOef2+S+/BNr4zwcD/3PObXfO7QD+Bwwp572JiIgcIW3bXqINWjZIwIDWSYk8fFHXgwtUi0hku33ICQCM+WhFwJGIhLdQ1hH80czqO+d2m9k9wInAX5xzC8s4rzVQeLRuOl4LX0muAT4s5Vz9Dy0iIhWyZfd+3pmfzs97JTP64m5BhyMiVaB1UiLXDEzhmemruXpACt2Tk4IOSSQshTJr6L1+EjgQOAtvgflnKzMIM7sc6AWMKed515nZfDObv2XLlsoMSUREaqCxs9PIycvnukHtgw5FRKrQjad1oGm9OB56fzk1Zak0kcoWSiJYMMf2ecALzrn3gbgQzssAkgu9buNvO4yZnQXcDQx1zu0vz7nOuRecc72cc72aNWsWQkgiIlJb7c7O4V9z1nJOl5a0b1Yv6HBEpArVT4jltrN+wtw125m6bFPQ4YiEpVASwQwzex64FPjAzOJDPG8ecLyZpZhZHHAZMKnwAWbWE3geLwncXGjXVOBsf7bSRsDZ/jYREZGj8uZX69idncsNp3YIOhQRqQaX9U7m+Ob1GP3hcg7k5gcdjkjYCSWhuwQvCRvsnMsEGgO3l3WScy4XGOWfuxx4xzm3zMweNLOh/mFjgHrAu2a22Mwm+eduB/6Ml0zOAx70t4mIiJTb/tw8Xp6VxoDjmtCtjcYLidQGMdFR/PHcjqzZto/Xv1wbdDgiYafMyWKcc/vMbDMwEPgeyPV/lsk59wHwQZFt9xV6flYp574CvBLKdURERErz34UZbN69n79f0iPoUESkGp2W2oxTjm/KE59+z8UntqFhndigQxIJG2W2CJrZ/cAfgLv8TbHA61UZlIiISGXJy3c8P/MHurZuyIDjmgQdjohUIzPjj+d2ZGdWDk9+GlI7hkitEUrX0J8BQ4G9AM65DUD9qgxKRESkskxd9iNpW/dyw6kdMLOgwxGRatbxmAZcclIyr81Zw9pte4MORyRshJIIHnDevLsOwMzqVm1IIiIilcM5x7PTV9OuSR2GdGkZdDgiEpDfnf0TYqOj+JsWmRc5KJRE8B1/1tAkM/s18DHwYtWGJSIiUnGzV29jacZOrj+1A9FRag0Uqa2aN0jg+kEd+GDpj8xfo/kHRSCERNA59ygwDhgPpAL3OeeerOrAREREKurZ6atpXj+ei05sHXQoIhKwXw9KoUWDeP6iReZFgNBaBHHO/c85dzswGq9FUEREJKwtTd/JrFVbGTkwhfiY6KDDEZGA1YmL4fdnp7J4fSaTl2wMOhyRwJWYCJpZXzObbmYTzKynmX0DfANsMrMh1ReiiIhI+T03YzX1E2IYcfKxQYciImHi4hPb0OmYBvztwxVk5+QFHY5IoEprEXwK+CvwH+BT4FrnXEtgEPBwNcQmIiJyVNK27uWDbzZyRd+21E/QumEi4omKMu45ryMZmVm8+sWaoMMRCVRpiWCMc26ac+5d4Efn3JcAzjlNtyQiImHthZmriY2O4uoBKUGHIiJhpv9xTTnzhOY889kqtu3ZH3Q4IoEpLRHML/Q8q8g+jbAVEZGwtHlXNuMXZPDzk9rQrH580OGISBi669yO7MvJ4x8fa5F5qb1KSwS7m9kuM9sNdPOfF7zuWk3xiYiIlMvLX6SRm5/PdYPaBx2KiISp45rX45d9juXNuetYtXl30OGIBKLERNA5F+2ca+Ccq++ci/GfF7zWgAsREQk7O7NyeOPLdZzXrRVtm9QNOhwRCWO3nXU8dWKjefgDjXqS2imk5SNEREQiwetfrmXP/lxuOFWtgSJSuib14rnp9OP4ZMVmZq/aGnQ4ItVOiaCIiNQI2Tl5vPpFGoN+0ozOrRoGHY6IRICrB7SjdVIif3l/OXn5mgJDahclgiIiUiOMW5DO1j0HuPHUDkGHIiIRIiE2mjuGpPLtxl1MWJgedDgi1UqJoIiIRLzcvHxemPkDPZKT6Nu+cdDhiEgEGdq9Fd2Tk3h02kr2HcgNOhyRaqNEUEREIt4H3/zIuu37uPG0DphZ0OGISAQxM+49ryObdu3nxZlpQYcjUm2UCIqISERzzvHs9NV0aFaXn3ZsEXQ4IhKBerVrzDldWvL8zNVs3pUddDgi1UKJoIiIRLSZ329l+cZdXH9qB6Ki1BooIkfnznNOICcvn8emfRd0KCLVQomgiIhEtGenr6JlgwSG9WgddCgiEsHaNqnLr/q1450F61m+cVfQ4YhUOSWCIiISsRau28GXP2zn2lNSiIvRf2kiUjE3n3EcDRJi+esHy3FOy0lIzab/NUVEJGI9N301DRNj+UWfY4MORURqgKQ6cdxy5vF8/v1Wpn+3JehwRKqUEkEREYlIqzbvZtq3m7iyX1vqxscEHY6I1BBX9G1LuyZ1+Ov7y8nNyw86HJEqo0RQREQi0vMzfiAhNoor+7cLOhQRqUHiYqK485wT+H7zHt6evz7ocESqjBJBERGJOBsys5i4OIPLeh9Lk3rxQYcjIjXM4M4t6dOuMY//7zt2Z+cEHY5IlVAiKCIiEeflWWnkO7hmYErQoYhIDWRm3H1eR7buOcBzM1YHHY5IlVAiKCIiESVz3wH+M3cdQ7u3IrlxnaDDEZEaqntyEhf2aMVLn6exITMr6HBEKp0SQRERiSj/mrOWfQfyuP7U9kGHIiI13O2DU3HAmKkrgw5FpNIpERQRkYix70Aur36RxpknNOeElg2CDkdEarg2jepwzcAU/rsogyXpmUGHUytMXJTBgNGfknLn+wwY/SkTF2UEHVKNpURQREQixjvz1rNjXw43nNYh6FBEpJa46bQONKkbx1/e1yLzVW3iogzumrCUjMwsHJCRmcVdE5aGVTJYkxJVLbwkIiIRIScvnxc/T6NX20b0btc46HBEpJaonxDLbT/9CfdO/IZp325icOeWQYdUY42ZupKsnLzDtmXl5PF/by/mgcnLiI2OIi46iriYKGKjzXsdE0VsdBTx/s/YaCMuJtr7WWj/wZ/RdvB5wfbCx8X6++Oio44o/5MVmxj94Qqyc7z1JQsSVYBhPVtX+/tVUUoERUQkIkxZsoGMzCwevLBz0KGISC3zi97JvDZ7DaM/XMHpqc2Ji1GnuqpQ0qQ8DriweysO5OVzINeRk5fPgdx876f/fO/+XA7k5ZPj79/v7z90rONAXn6lx5yVk8eYqSuVCIqIiFQF5xzPTf+B1Bb1OT21edDhiEgtExMdxR/PPYGRY+fzxldruXqAlq6pbHv35xIXE8X+3COTtdZJifzpwi4VvoZzjpy84hPJnDzHgVzvdeH9h5JKx+/f/brYciN1VlklgiIiEvY+W7mZlZt28/dLuhMVZUGHIyK10OmpzRl4XFP++cn3XNSzDQ3rxAYdUo2xdc9+Ro6dx/7cfGKjjZy8Q2MxE2OjuX1waqVcx8yIi/G6ftaNL//5j//vOzKKSfpaJSVWQnTVT+3aIiIS9p6dvprWSYlc0L1V0KGISC1lZvzx3I7szMrhyU+/DzqcGmPttr1c/Oxsvtu0m5d+1Ysxw7vTOikRw2sJfPiirmHT7fL2wakkxkYftq0yE9XqphZBEREJa/PWbGfemh08cEEnYqP1/aWIBKdTqwb8/KQ2vDZnDVf0a0vbJnWDDimiLUnP5OpX55HnHG9c25eT2jYCwnfilYK4xkxdyYbMLFolJXL74NSwjbcsSgRFRCSsPTd9NY3rxnFp72ODDkVEhN+dncrkrzfyt49W8MyIk4IOJ2LN+G4LN76+gEZ14vjXNX3o0Kxe0CGFZFjP1hGb+BWlr1ZFRCRsrfxxN5+s2MyV/dqRGBdd9gkiIlWsRYMErj+1PR8s/ZH5a7YHHU5EGr8gnWvGzqNtk7r896b+EZME1jRKBEVEJGw9P2M1deKi+VW/tkGHIiJy0HWD2tOiQbwWmS8n5xzPTF/F7979mpPbN+ad6/vSvEFC0GHVWkoERUQkLKXv2Md7X2/gF32OpVHduKDDERE5qE5cDL87O5XF6zOZvGRj0OFEhLx8xwOTlvHIRysZ2r0Vr17Vh/oJmnk1SEoERUQkLL30eRpRBteeovW6RCT8XHxiGzoe04C/fbiC7Jy8oMMJa9k5edz8n4W8Nmctvz4lhX9c2oO4GKUhQavS34CZDTGzlWa2yszuLGb/IDNbaGa5Zja8yL48M1vsPyZVZZwiIhJetu89wFvz1nFhj9Yc0zAy12cSkZotOsq457yOZGRmMXb2mqDDCVs7s3L41Stz+WDpj9xzXkfuPq+T1oMNE1WWCJpZNPA0cA7QCfiFmXUqctg64CrgzWKKyHLO9fAfQ6sqThERCT9jZ68hOyefG05tH3QoIiIlGnBcU844oTlPf7qKbXv2Bx1O2Nm4M4tLnpvDonU7+OdlPbj2FP2bHk6qskWwD7DKOfeDc+4A8BZwYeEDnHNrnHNLgPwqjENERCLI3v25vDZ7DWd3asFxzesHHY6ISKn+eO4J7MvJ45+faJH5wr7btJuLnpnttZhe3YcLe9SMJRdqkqpMBFsD6wu9Tve3hSrBzOab2ZdmNqxyQxMRkXD1n7nr2JmVww2ndQg6FBGRMh3XvD6/6JPMG1+tY9XmPUGHExbmrdnO8Gdnk5vvePv6vgw4rmnQIUkxwnmUZlvnXC/gl8A/zOyITwRmdp2fLM7fsmVL9UcoIiKV6kBuPi/PSuPklMaceGyjoMMREQnJbWf9hMTYaEZ/uDzoUAL30TcbGfHSVzStH8+EG/vTuVXDoEOSElRlIpgBJBd63cbfFhLnXIb/8wdgOtCzmGNecM71cs71atasWcWiFRGRwL23OIONO7O5Ua2BIhJBmtaL56bTO/Dx8s3MXr016HAC8+85a7jxjYV0btWAcTf0J7lxnaBDklJUZSI4DzjezFLMLA64DAhp9k8za2Rm8f7zpsAA4Nsqi1RERAKXn+94bsZqOh7TgFN/oi/3RCSyjByQQuukRB56fzn5+bVrkXnnHGOmruDe95ZxRmpz3ry2L421/mvYq7JE0DmXC4wCpgLLgXecc8vM7EEzGwpgZr3NLB34OfC8mS3zT+8IzDezr4HPgNHOOSWCIiI12P+Wb2L1lr3ceFoHzDS1uIhEloTYaO4YksqyDbuYsCjkTnARLycvn9vHLeHpz1ZzWe9knr/iJBLjooMOS0JgztWMbyx69erl5s+fH3QYIiJyFJxz/OyZ2Wzbu5/PfncaMdHhPIRdRKR4+fmOU8d8RkZmFs5Bq6REbh+cyrCeNXPGzL37c/nNmwuZvnILt511PLeeeby+yAuYmS3w51kpU0xVByMiIlKWr9K2s3h9Jn8e1kVJoIhErElfb2DT7v0U9AzNyMzirglLAWpcMrh1z35Gjp3HNxk7efiirvyiz7FBhyTlpP9tRUQkcM9OX03TenH8/KQ2QYciInLUxkxdyYHcw5fHzsrJ45GPVgQUUdVYu20vw5+dzXebdvPCFb2UBEYoJYIiIhKoZRt2MuO7LVw9IIWEWI0rEZHItSEzq/jtO7O5+79LWbhuB5E+LGtp+k4ufnY2mVk5vHFtX87q1CLokOQoqWuoiIgE6vkZP1AvPobL+7YNOhQRkQpplZRIRjHJYGJsNOMXpvPGV+to36wuF5/YhotObM0xDRMDiPLozfhuCze+voBGdeJ4a2QfjmteL+iQpALUIigiIoFZt20fU5ZsYMTJx9IwMTbocEREKuT2wakkFunZkBgbzcMXdWXe3WfxyMXdaFovnjFTV9J/9Kdc8fJXTFyUQdaBvIAiDt2EhelcM3YebZvUZcJN/ZUE1gBqERQRkcC88PlqYqKiGDkwJehQREQqrGBCmDFTV7IhM+uIWUMv6Z3MJb2TWbdtH+MXpjN+YTq3vb2YevExnNf1GIb3akOvto3CauZN5xzPzfiBv320gv4dmvDcFSfRIEFf3NUEWj5CREQCsWX3fgb87VMu6tma0Rd3CzocEZFql5/vmLtmO+MWpPPB0o3sO5BH2yZ1DnYdbdOoTqDx5eU7/jzlW8bOXsMF3Vvx6M+7ER+jsdzhrDzLRygRFBGRQIyZuoJnpq/mk9+eSvtm6mIkIrXb3v25fPTNj4xfmM7s1dsA6Ne+CRef1IZzurSkbnz1duTLzsnjt+8s5oOlP3LtwBT+eG5HoqLCp6VSiqdEUEREwtru7Bz6j/6UU45vyjMjTgo6HBGRsJK+Yx//XZjBuIXprN22jzpx0ZzT5RguPqk1fVOaVHlCtjMrh+v+NZ+v0rZz97kd+fWg9lV6Pak8WlBeRETC2ptfrWN3di43nNoh6FBERMJOm0Z1uPnM4xl1xnEsWLuD8QvTmfL1RsYvTKd1UiIXn9iai09qQ9smdSv92j/uzObKV+byw9Y9/POyHlzYo3WlX0PCg1oERUSkWu3PzeOUv33G8S3q8ca1fYMOR0QkImTn5DF12Y+MW5DOrFVbcQ56t2vE8JPacG7XY6hfCRO4fL9pN1e+Mpdd2bk8f8VJDDiuaSVELtVJLYIiIhK2/rswg8279/P3S3oEHYqISMRIiI3mwh6tubBHazbuzOK/izIYvyCdP4xfyv2TljGkc0suPqkN/Ts0Jfoouo7OW7Oda1+bT1xMFG9f35fOrRpWwV1IOFGLoIiIVJu8fMdZf59BvfgYJo0aEFZTpIuIRBrnHIvXZzJ+YTqTFm9gV3YuxzRM4Gc9va6jHUKciOujb37k1rcW0TopkddG9iG5cbCzlcrR02QxIiISlj5YupGb3ljIMyNO5NyuxwQdjojUMjk5OaSnp5OdnR10KJXOOUd2Tj57D+SyPycfB8TFRFE3LprEuGiiSvjibc/+XHbuyyE2JoqmdeM0M2iESEhIoE2bNsTGHt4lWF1DRUQk7HiLEq8mpWldBnduGXQ4IlILpaenU79+fdq1a1ejeyTk5OWTuS+HHfsOkJ2TR74Z9RNiSKobR16eY9OubA7k5RNtRrxzHJcQy7GN6ygJjBDOObZt20Z6ejopKSlHXY4SQRERqRazV29jSfpOHr6o61GNXxERqajs7OwanwQCxEZH0ax+PE3rxZGdk8eOfTlk7jtAZlbOYcflOYdhNEyMVRIYQcyMJk2asGXLlgqVE1VJ8YiIiJTq2emraV4/notO1FTkIhKcmp4EFmZmJMbF0CopkROOaUBM1JEf/R1eC6FElsqox0oERUSkyi1N38msVVu5ZmAK8THRQYcjIlLrRJmRm59f7L4DecVvl5pNiaCIiFS552aspn5CDL88+digQxERCdSaNWvo0qVLlZQ9ffp0zj//fAAmTZrE6NGjD9sfF138R/+Stkey8r7PY8eOZcOGDWUeM2rUqIqGFjY0RlBERKpU2ta9fPDNRm48tUOlLHgsIlJdJi7KYMzUlWzIzKJVUiK3D05lWM/I6N4+dOhQhg4deti2Fg0TyNiRRX6hVQOizGjRMKG6wzvcknfgkwdhZzo0bANn3gfdLqnWEMaOHUuXLl1o1apVtV4XIDc3l5iY6k/Lal76LyIiYeWFmauJjY7i6gFHP7OZiEh1m7gog7smLCUjMwsHZGRmcdeEpUxclFHhsnNzcxkxYgQdO3Zk+PDh7Nu3jwcffJDevXvTpUsXrrvuOgqWeHviiSfo1KkT3bp147LLLgNg7969jBw5kj59+tCzZ0/ee++9I65RuPXqqquu4pZbbuG8s07j/IE9+OzDSYDXEjh+7DOcfeoAunXrxv3331/heyu3Je/A5Ftg53rAeT8n3+Jtr6BQ3+dx48Yxf/58RowYQY8ePcjKymLevHn079+f7t2706dPH3bv3g3Ahg0bGDJkCMcffzx33HHHwWvVq1ePu+++m+7du9O3b182bdoEeC2TZ5xxBt26dePMM89k3bp1gPc7ueGGGzj55JO54447eOCBB7jyyis55ZRTaNu2LRMmTOCOO+6ga9euDBkyhJycnCNvsILUIigiIlVm865sxi/I4JLebWhWPz7ocEREDvrT5GV8u2FXifsXrcs8YuxcVk4ed4xbwn/mriv2nE6tGnD/BZ3LvPbKlSt5+eWXGTBgACNHjuSZZ55h1KhR3HfffQBcccUVTJkyhQsuuIDRo0eTlpZGfHw8mZmZADz00EOcccYZvPLKK2RmZtKnTx/OOuusUq+5ceNGZs2axYoVKxg6dCi3/vpXTJs2jYy1acydOxfnguPmTAAAIABJREFUHEOHDmXmzJkMGjSozHsI2Yd3wo9LS96fPg/y9h++LScL3hsFC14r/pyWXeGc0cXvKyTU93n48OE89dRTPProo/Tq1YsDBw5w6aWX8vbbb9O7d2927dpFYmIiAIsXL2bRokXEx8eTmprKzTffTHJyMnv37qVv37489NBD3HHHHbz44ovcc8893HzzzVx55ZVceeWVvPLKK9xyyy1MnDjRu/X0dGbPnk10dDQPPPAAq1ev5rPPPuPbb7+lX79+jB8/nkceeYSf/exnvP/++wwbNqzs97sc1CIoIiJV5uUv0sjNz+e6UzoEHYqISLmUNIFKZUyskpyczIABAwC4/PLLmTVrFp999hknn3wyXbt25dNPP2XZsmUAdOvWjREjRvD6668f7D44bdo0Ro8eTY8ePTjttNPIzs4+2NJUkmHDhhEVFUWnTp0OtlZNmzaNadOm0bNnT0488URWrFjB999/X+H7K5eiSWBZ28uhPO9zYStXruSYY46hd+/eADRo0ODge3/mmWfSsGFDEhIS6NSpE2vXrgUgLi7u4PjMk046iTVr1gAwZ84cfvnLXwJe4jlr1qyD1/n5z39OdPShCdTOOeccYmNj6dq1K3l5eQwZMgSArl27HiyvMqlFsIpEQp/ycI9R8VWM4qsYxVcxExdl8LePVrBxZzaJsdEsXLeDY5vUCTosEZGDymq5GzD6UzIys47Y3jopkbev71ehaxed+t/MuOmmm5g/fz7Jyck88MADZGd7Szq8//77zJw5k8mTJ/PQQw+xdOlSnHOMHz+e1NTUw8opSPCKEx9/qFdGQbdT5xx33XUX119/fYXup1Rltdw93sXvFlpEw2S4+v0KXbo873OoCr+P0dHR5ObmAhAbG3vweoW3l6Zu3brFlh0VFXVYeVFRUSGVV15KBKtAQZ/yrJw8wOtT/ofxS0jbuodBP2kWcHSemd9t4bkZP7A/1/tWK9xiVHwVo/gqRvFVTNH4snLyuGuC1y0onJJVEZHS3D449bDPcwCJsdHcPji1lLNCs27dOubMmUO/fv148803GThwILNnz6Zp06bs2bOHcePGMXz4cPLz81m/fj2nn346AwcO5K233mLPnj0MHjyYJ598kieffBIzY9GiRfTs2bPccQwePJh7772XESNGUK9ePTIyMoiNjaV58+YVvseQnXmfNyYwp1DSHZvoba+gUN9ngPr16x8cB5iamsrGjRuZN28evXv3Zvfu3Qe7hpZX//79eeutt7jiiit44403OOWUUyp8X5VFiWAVGDN15WH/aADsz83nn5+s4p+frAooqrKFe4yKr2IUX8UovorJysljzNSVSgRFJGIU/HtVFb0vUlNTefrppxk5ciSdOnXixhtvZMeOHXTp0oWWLVse7JKYl5fH5Zdfzs6dO3HOccstt5CUlMS9997LbbfdRrdu3cjPzyclJYUpU6aUO46zzz6b5cuX06+f18JZr149Xn/99epNBAtmB62CWUNDfZ/h0OQtiYmJzJkzh7fffpubb76ZrKwsEhMT+fjjj48qhieffJKrr76aMWPG0KxZM1599dUK31dlMVdo+thI1qtXLzd//vygwwAg5c73Keld/dfIPtUaS0l+9crcEveFQ4yKr2IUX8UovoopKT4D0kafV73BiIgUsnz5cjp27Bh0GCKVorj6bGYLnHO9QjlfLYJVoFVSYol9ysOh2xZ4sYRzjIqvYhRfxSi+iikpvlZJR9etRkRERCqfZg2tArcPTiUxNvqwbZXVp7yyhHuMiq9iFF/FKL6KCff4RERERC2CVaIq+5RXlnCPUfFVjOKrGMVXMeEen4jUbs65I2aTFIk0lTG8T2MERURERKRWSEtLo379+jRp0kTJoEQs5xzbtm1j9+7dpKSkHLZPYwRFRERERIpo06YN6enpbNmyJehQRCokISGBNm3aVKgMJYIiIiIiUivExsYe0YIiUltpshgREREREZFaRomgiIiIiIhILaNEUEREREREpJapMbOGmtkWYG0ZhzUEdoZYZKjHlnVcU2BriNeMZOV5byM1hsoqv6LllPf8IOo91I66r3pffWVVZb0P9XjVe0841Huo2jgitd6X9xzV+9DVhnpfmeVHer0P5bhwrfdtnXPNQjrSOVdrHsALlX1sWccB84O+73B7byM1hsoqv6LllPf8IOq9f0yNr/uq99VXVlXW+1CPV72v/DoRrnFEar0v7zmq98HUiXCOIxw+64RDvQ/luJpQ72tb19DJVXBsecqsycLhfajqGCqr/IqWU97zVe+rTji8D5FS7ytaVlXW+1CPD4ffdzgIl/ehKuOI1Hpf3nNU70MXLu9DpPybH+n1/mjjiCg1pmtouDKz+S7ERR1FahLVfamNVO+lNlK9l9qoJtT72tYiGIQXgg5AJCCq+1Ibqd5LbaR6L7VRxNd7tQiKiIiIiIjUMmoRFBERERERqWWUCIqIiIiIiNQySgRFRERERERqGSWCIiIiIiIitYwSwYCZWV0zm29m5wcdi0h1MLOOZvacmY0zsxuDjkekupjZMDN70czeNrOzg45HpDqYWXsze9nMxgUdi0hV8j/Tv+b/Oz8i6HhCoUTwKJnZK2a22cy+KbJ9iJmtNLNVZnZnCEX9AXinaqIUqVyVUe+dc8udczcAlwADqjJekcpSSXV/onPu18ANwKVVGa9IZaikev+Dc+6aqo1UpGqU82/gImCc/+/80GoP9iho+YijZGaDgD3Av5xzXfxt0cB3wE+BdGAe8AsgGni4SBEjge5AEyAB2Oqcm1I90Yscncqo9865zWY2FLgR+Ldz7s3qil/kaFVW3ffPewx4wzm3sJrCFzkqlVzvxznnhldX7CKVoZx/AxcCHzrnFpvZm865XwYUdshigg4gUjnnZppZuyKb+wCrnHM/AJjZW8CFzrmHgSO6fprZaUBdoBOQZWYfOOfyqzJukYqojHrvlzMJmGRm7wNKBCXsVdK/+QaMxvugoCRQwl5l/ZsvEqnK8zeAlxS2ARYTIb0ulQhWrtbA+kKv04GTSzrYOXc3gJldhdciqCRQIlG56r3/BchFQDzwQZVGJlK1ylX3gZuBs4CGZnacc+65qgxOpIqU99/8JsBDQE8zu8tPGEUiWUl/A08AT5nZecDkIAIrLyWCYcA5NzboGESqi3NuOjA94DBEqp1z7gm8DwoitYZzbhveuFiRGs05txe4Oug4yiMimi0jSAaQXOh1G3+bSE2mei+1leq+1Eaq91Lb1Zi/ASWClWsecLyZpZhZHHAZMCngmESqmuq91Faq+1Ibqd5LbVdj/gaUCB4lM/sPMAdINbN0M7vGOZcLjAKmAsuBd5xzy4KMU6Qyqd5LbaW6L7WR6r3UdjX9b0DLR4iIiIiIiNQyahEUERERERGpZZQIioiIiIiI1DJKBEVERERERGoZJYIiIiIiIiK1jBJBERERERGRWkaJoIiIiIiISC2jRFBERCqNmT1uZrcVej3VzF4q9PoxM/ttGWXMDuE6a8ysaTHbTzOz/iWcM9TM7iyj3FZmNs5/3sPMzi3n+VeZ2VP+8xvM7Fdl3UtZ93C05VQFP7YpQcchIiIVFxN0ACIiUqN8AVwC/MPMooCmQINC+/sD/1daAc65YhO5EJ0G7AGOSCadc5OASWVcewMw3H/ZA+gFfBDq+UXKei7UY4s4jUL3UIFyRERESqQWQRERqUyzgX7+887AN8BuM2tkZvFAR2AhgJndbmbzzGyJmf2poAAz2+P/jDKzZ8xshZn9z8w+MLPhha51s5ktNLOlZnaCmbUDbgD+z8wWm9kphQMr0lo31syeMLPZZvZDQblm1s7MvjGzOOBB4FK/rEuLnH+BmX1lZovM7GMza1H0jTCzB8zs934r4+JCjzwza1tcGcXdQ0E5fpk9zOxL/z37r5k18rdPN7O/mdlcM/uu6L37xxxjZjP9cr8pOMbMhvjv49dm9om/rY+ZzfFjm21mqcWUV9fMXvGvucjMLiyxVoiISNhRIigiIpXGb1HLNbNj8Vr/5gBf4SWHvYClzrkDZnY2cDzQB6/l7SQzG1SkuIuAdkAn4AoOJZgFtjrnTgSeBX7vnFsDPAc87pzr4Zz7vIxwjwEGAucDo4vcxwHgPuBtv6y3i5w7C+jrnOsJvAXcUdJFnHMb/DJ6AC8C451za4srI4R7+BfwB+dcN2ApcH+hfTHOuT7AbUW2F/glMNWPozuw2Mya+TFd7JzrDvzcP3YFcIof233AX4sp727gU/+apwNjzKxuSe+DiIiEF3UNFRGRyjYbLwnsD/wdaO0/34nXdRTgbP+xyH9dDy8xnFmonIHAu865fOBHM/usyHUm+D8X4CWN5TXRL/vb4lr0ytAGeNvMjgHigLSyTjCzAcCv8e6r3GWYWUMgyTk3w9/0GvBuoUMKvx/tiiliHvCKmcXi3ftiMzsNmOmcSwNwzm33j20IvGZmxwMOiC2mvLOBoQWtlUACcCywvLT7EBGR8KAWQRERqWxf4CV+XfG6hn6J15rXn0Nj9wx4uKClzDl3nHPu5XJeZ7//M4+j+2Jzf6HnVs5znwSecs51Ba7HS4JK5Cd7LwOXOOf2HE0ZISj1/XDOzQQGARnA2DImoPkz8JlzrgtwQQmxGV5LYsHv8FjnnJJAEZEIoURQREQq22y87pbbnXN5fitTEl4yWJAITgVGmlk9ADNrbWbNi5TzBXCxP1awBd4kKmXZDdSvhHsoq6yGeAkVwJWlFeK3wL2L16XzuxDKKPa6zrmdwI5C4/+uAGYUPa6UONoCm5xzLwIvASfiJemDzCzFP6ZxMbFdVUKRU/HGaZp/bs9QYxERkeApERQRqSHMbI+ZtQ86Dryxa03xkozC23Y657YCOOemAW8Cc8xsKTCOI5Of8UA68C3wOt4kMzvLuPZk4GfFTRZzFD4DOhVMFlNk3wPAu2a2ANhaRjn98cZH/qnQhDG/8fcVV0bRe+gBFBx/Jd5YvCX+9gfLugkzO9afgOd04GszWwRcCvzTObcFuA6YYGZfA+/5xz4KPOwfW7R1cYhfz/6M12V0iZkt818Xvm47M3NmFuO//tDMSk2aj4aZLfO7uIY1f7KhWUHHISJSwJxzQccgIhIWzGwN0AKva10OXuvVDc659ZVQ7rXOuY9L2H8a8Lpzrk1FrlMTmVk959weM2sCzAUGOOd+DDqu6mRmV+HVn4El7J+OV39eKm5/Ba991GX7M6CmAbHOudxKimcskO6cu6cyyqtOZf0eRUSqm1oERUQOd4Fzrh7ejJKb8MZxBa6gVaUmKuPeppjZYuBz4M+1LQkUERGpKkoERUSK4ZzLxuuu2Klgm5nFm9mjZrbOzDaZ2XNmlujva2pmU8ws08y2m9nn/ti2f+PNpDjZ77p52DID/nT7HwKt/P17zFt37gEzG2dmr5vZLuCqQmu7ZZrZRjN7yrz17grKcmZ2nP98rJk9bWbvm9lu89ar61DS/ZrZu2b2o5ntNG+tuc6F9iWa2WNmttbfP6vQfQ80b525TDNb77d6FKxrd22hMg7rFufH+hsz+x743t/2T7+MXWa2wMxOcc6d5i930NV/j1b797PAzJL9e3ysyL1MMrMjFq03s2fN7NEi294zs9/6z/9gZhl++SvN7Mxiykjx7zXKf/2imW0utP/fZnab/7yhmb3s/64yzOwvZhZdwvtxtn/NneatnTij8PvnH/Oome0wszQzO8ff9hBwCvCUX3eeKibmol00p5vZn83sC/9ep5lZ06LHllR2kXp2nnlrCO7yf3cPFL1+oTgO1gnz1izcU+jhzO/eWVJdNLPrgBHAHf45k/3ta8zsLP95vJn9w8w2+I9/mLd+JWZ2mpmlm9nvzGyz/3u5upR4rzJvjcnd/ns+otC+X5vZcn/ft2Z2or/9zkJ19Fsz+1kp5Z9g3vqY2/3f/SUlHSsiUhWUCIqIFMPM6uCNoyo8zm008BO8sVnH4S2LcJ+/73d449ma4XUv/SPgnHNXAOvwWxqdc48Uvo5zbi9wDrDB31/PX4sP4EK8ZDQJeAOvy+r/4Y2/6wecCdxUym1cBvwJaASsAh4q5dgP8ZZvaI43Fu+NQvseBU7CG+vWGG/NvHzzJh/5EK/VtJn/viwu5RpFDQNO5lCyPc8vozHe+MF3zaxgtsrfAr8AzgUaACOBfXhLKPyiUGLWFDjLP7+o/+AtEF8wuUkjvCUQ3jJvwfRRQG/nXH1gMLCmaAH+Mgu7gIKJUQYBe8yso//6VA5N4DIWyMWrKz39ax2W3BWKeRxwF9AEWIn3Xhd2sr+9KfAI8LKZmXPubrzW0lF+3RlVzH0X55fA1Xi/7zjg90UPCLHsvcCv8OroecCNZjasrIs757oX1He83+1KvHoHJdRF59wL/vNH/HMvKKbou4G+ePWoO946lYW7kbbEmwinNXAN8LRfDw5j3hc0TwDn+PWhP37dNrOf440R/RVeXRwKbPNPXY2XPDfE+9t73bwZY4sr/3949bQ53t/qM2bWqeixIiJVRYmgiMjhJppZJt6kJD8FxgD4ycN1wP8557Y753bjLbJ9mX9eDl530rbOuRzn3Oeu4oOw5zjnJjrn8p1zWc65Bc65L51zuf7C48/jJR4l+a9zbq4/PusNvA/HxXLOveKc2+2c24/3Ibe736IVhZd03eqcy/BnAZ3tH/dL4GPn3H/8e97mnCtPIviw/15m+TG87peR65x7DIgHUv1jrwXucc6tdJ6v/WPn4v2uClrvLgOmO+c2FXO9z/HWxCuYRGY43nu8AS/JjsebHCbWObfGObe6hLhnAKeaWUv/9Tj/dQpeYvC1ebOcngvc5pzb65zbDDzOofpS2LnAMufcBP939QRQtAvsWufci865PLzk9xi8LxyO1qvOue/89/4dSqkbpXHOTXfOLfXr6BK8ZLu0OnkYMxsI/AUY6pzb5ZdZbF0MscgRwIPOuc3+RDh/wptdtUCOvz/HOfcBsIdDdayofKCLmSU65zY655b526/FS0bn+XVxlXNurR/7u865Df778TZea3efYso+H1jjnHvVr++L8CZH+nmI9ykiUmFKBEVEDjfMOZeEt27aKGCG/4G/GVAHWGBe18BM4CN/O3gJ4ypgmt+d7M5KiOWwSWrM7CfmdT/90bzuon/FayEqSeFkYh/eou1HMLNoMxvtd2nbxaGWsKb+IwGvpaOo5BK2h6ro/f3e7263039/G3Lo/kq71mvA5f7zy4F/F3eQn5i/hdeyCF4iW9DatAq4DS/x2Gxmb5lZqxKuNwNvKYtBwExgOl7ycyrwufMWqW+LN6PmxkL15Xm81p+iWlHovfDjTC9yzI+F9u/znxb7+wxRSHWjLGZ2spl9ZmZbzGwncAOl18nC5ybjJaFXOn9ZjTLqYihaAWsLvV7rbyuwrcjENcXeu99Sfyne/Ww0r4v1Cf7uEuuimf3KvNleC37nXUqIvS1wcsFx/rEj8FosRUSqhRJBEZFi+C1fE/BaigbiTe+fBXR2ziX5j4Z+1zb8FozfOefa43UV+60dGmNWVstgSfuLbn8WWAEc75xrgNf9tLwLoRfnl3jdUM/CS77a+dsN776zgeLGF64vYTt4XQbrFHpd3Afcg/dn3jIJdwCXAI38ZHwnh+6vtGu9DlxoZt2BjsDEEo4Dr8VquN+t9WS8VhgvGOfedN6Mjm392P5WQhkz8FoVT/OfzwIGcHi30PV4C7w3LVRfGjjnOhdT3kbg4IyxfutzeWaQrcrpv8sq+01gEpDsnGsIPEcIddK8MaYTgX845z4stKu0uhhKPBvwfn8FjvW3lZtzbqpz7qd4ra8rgBf9XcXWRb9OvYj3BVITvw5/Q/Hvx3pgRqG6keR3d73xaGIVETkaSgRFRIphngvxxtct91t5XgQeN3/hc/MWQR/sPz/fzI7zP8TvxEsg8/3iNgGlre+3CWgSQve3+njj0/b4rROV9aGxPl7Ssg0veftrwQ7/vl8B/m7eJDbRZtbPn4DjDeAsM7vEvMlFmphZQRfDxcBFZlbHvIlFrgkhhlxgCxBjZvfhdbMs8BLwZzM73v/ddDNvSQmcc+l44wv/DYwv6GpaHL8L3la/vKnOuUwAM0s1szP8+8rGS/rzSyjje3//5Xgf5nfh/Q4vxk8EnXMbgWnAY2bWwLyJgzqYWXHdJt8HuprZMPMmdPkN5WsZKqt+VURZZdcHtjvnss2sD14iF4pXgBWuyJhZSqmLIcbzH+AeM2vmj728D++LgnIxsxZmdqE/lm8/XhfSgvrwEvB7MzvJr4vH+UlgXbxEdYtfxtV4LYLFmQL8xMyuMLNY/9HbDo01FRGpckoERUQON9m8BbV34U2ucmWhsUF/wOv++aXfbe1jDo0vOt5/vQeYAzzjnPvM3/cw3ofTTDMrblKOFXgfYH/wjympS+Lv8T5o78ZLSt+u2K0e9C+8LnQZeIu3f1lk/+/xFoSfB2zHaymLcs6twxvf9jt/+2K8CTrAGw93AO+D+2scPvlMcabidbX9zo8lm8O7jv4drxvhNLzfzctAYqH9r+HNLFpst9Ai3uTICWXi8SYD2orXbbI53uQtJZmB181wfaHXxqEJT8CbTCQO7z3dgTeW8IiJQ5xzW/HGhj2ClwB1AubjJSCh+CdeK+cOM3sixHNCVVbZNwEPmtluvKTrnRDLvQz4mR0+c+gplF0XX8Ybx5lpZsW1/P4F771bgldnF/rbyisKbxKbDXh1+1T8L16cc+/i/dvwJt7f4kSgsXPuW+AxvL//TXj18YviCnfeGOOz/fdhA16d+xtePRQRqRZaUF5ERCKemQ3Ca/lp6yL8PzbzJuhJB0YU+jJBRESkUqlFUEREIpqZxQK3Ai9FahJoZoPNLMnvmlow9rNoa5iIiEilUSIoIiIRyx9TlYnX5fIfAYdTEf3wZqLcClyAN3ttiWMdRUREKkpdQ0VERERERGoZtQiKiIiIiIjUMjFBB1BZmjZt6tq1axd0GCIiIiIiIoFYsGDBVudcs1COrTGJYLt27Zg/f37QYYiIiIiIiATCzNaGeqy6hoqIiIiIiNQySgRFRERERERqGSWCIiIiIiIitUyNGSNYnJycHNLT08nOzg46lBotISGBNm3aEBsbG3QoIiIiIiISghqdCKanp1O/fn3atWuHmQUdTo3knGPbtm2kp6eTkpISdDgiIiIiIhKCGt01NDs7myZNmigJrEJmRpMmTdTqKiIiIiISQWp0IggoCawGeo9FRESk2ix5Bx7vAg8keT+XvBN0RCIRqUZ3DRURERGRGmTJOzD5FsjJ8l7vXO+9Buh2SXBxiUSgGt8iGLQ1a9bQpUuXKil7+vTpnH/++QBMmjSJ0aNHV8l1RERERMLCJw8eSgIL5GR520WkXNQiWMjERRmMmbqSDZlZtEpK5PbBqQzr2TrosEIydOhQhg4dGnQYIiIiIpXLOfhxKaz+xGsBLM7O9fDZXyG5D7TpDQkNqzdGkQikFkHfxEUZ3DVhKRmZWTggIzOLuyYsZeKijAqXnZuby4gRI+jYsSPDhw9n3759PPjgg/Tu3ZsuXbpw3XXX4ZwD4IknnqBTp05069aNyy67DIC9e/cycuRI+vTpQ8+ePXnvvfeOuMbYsWMZNWoUAFdddRW33HIL/fv3p3379owbN+7gcWPGjKF3795069aN+++/v8L3JiIiIlLp9mzxuoFOuB4e/Qk8fwp8/ABElbBUVVQszBwDr18Mo9vCM/1h8m2w+D+wbbWXTIrIYWpNi+CfJi/j2w27Sty/aF0mB/LyD9uWlZPHHeOW8J+564o9p1OrBtx/Qecyr71y5UpefvllBgwYwMiRI3nmmWcYNWoU9913HwBXXHEFU6ZM4YILLmD06NGkpaURHx9PZmYmAA899BBnnHEGr7zyCpmZmfTp04ezzjqr1Gtu3LiRWbNmsWLFCoYOHcrw4cOZNm0a33//PXPnzsU5x9ChQ5k5cyaDBg0q8x5EREREqkzuAUifC6s+8Vr+Nn7tbU9sDB3OgOPO9H6mzTx8jCBAbCJc8ASkngMZC2D9XFj/FXwzARa86h1Ttxkkn+y1GCafDMf0gNiE6r9PkTBSaxLBshRNAsvaXh7JyckMGDAAgMsvv5wnnniClJQUHnnkEfbt28f27dvp3LkzF1xwAd26dWPEiBEMGzaMYcOGATBt2jQmTZrEo48+CnjLYqxbV3xyWmDYsGFERUXRqVMnNm3adLCcadOm0bNnTwD27NnD999/r0RQREREqt+21bD6U++RNhMO7IGoGGjTB864Bzqc6SVsUYU6sBVMCPPJg7AzHRq2gTPvO7S9/WneAyA/H7as8JLCgseKKd6+6Div7OQ+cGxfLzms17x67lskTNSaRLCslrsBoz8lIzPriO2tkxJ5+/p+Fbp20eUVzIybbrqJ+fPnk5yczAMPPHBwHb7333+fmTNnMnnyZB566CGWLl2Kc47x48eTmpp6WDkFCV5x4uPjDz4v6HbqnOOuu+7i+uuvr9D9iIiIiJTb/t1ewlfQ6rdjjbc9qa2XyHU4E1IGQUKD0svpdkloM4RGRUGLTt6j19Xetj1bvJbHdV96LYdzX4Q5T3n7GrWD5L6HWg2bd4So6KO9W5GwV2sSwbLcPjiVuyYsJSsn7+C2xNhobh+cWspZoVm3bh1z5syhX79+vPnmmwwcOJDZs2fTtGlT9uzZw7hx4xg+fDj5+fmsX7+e008/nYEDB/LWW2+xZ88eBg8ezJNPPsmTTz6JmbFo0aKDrXrlMXjwYO69915GjBhBvXr1yMjIIDY2lubN9Q2YiIiIVLL8fPjxaz/x+9RrkcvPhdi6kHIK9P2N1+WzcXuorjWJ6zWDE87zHgC5+2HjElj/pRff6k9hyVvevvgG0KbXoS6lrXuVnaSKRBAlgr6C2UGrYtbQ1NRUnn76aUaOHEmnTp248cYb2bFjB126dKFly5b07t0qZFPbAAAgAElEQVQbgLy8PC6//HJ27tyJc45bbrmFpKQk7r33Xm677Ta6detGfn4+KSkpTJkypdxxnH322Sxfvpx+/bwWznr16vH6668rERQREZHKsXuT393zE1j9Gezb6m1v2Q363+yN80vuCzFxwcZZICYeknt7D272JpXZsebQOMP1c2H6aMCBRUHzzodaDI892WvNLJrELnmn5K6rImHEXA2ZRalXr15u/vz5h21bvnw5HTt2DCii2kXvtYiISC2Uu9/rZrn6E1j1KWxa6m2v28xL+jqcCR1Oj+zxd9m7IGN+oeRwHhzY7e2r18JPDP1xhtu+h/d/W/xkNkoGpRqY2QLnXK9QjlWLoIiIiIgcUlqLlnOwbdWh7p5rPoecfd7yDcf2hTPv97p7tuh6+CQvkSyhgZ/UnuG9zs+DzcsPn4Rm+eSSz8/Jgv/d581qGlev+rrBipRBiaCIiIiIeJa8c/jyDDvXw6RbvGUZcrO9Vr+d/szljdtDjxFe4tduIMTXDy7u6hQVDS27eI/e13jbdm/yJqF5+/Liz9m9ER5u482KmpAEiY38R6Hnh20vZl90JX1sV9dV8SkRFBERERHPJw8e3q0RIDcLvnoO4up7s3oOvNXr8tk4JZgYw1H9FtDxAmiY7CXPRSU2goG/hexMyNpx6LFnM2xZCVmZsH9n6deIq18oQSySNJaWRMbWOdQKWVyiP/kW77mSwVpHiaCIiIiIeHaml7DD4A9pEB1breFEnDPvK37B+3MeKTvRysuF/bsKJYpFksaiSeTmFYee5+eUXG5U7KGkcEca5B04fH9OlvcFgBLBWkeJoIiIiIh46jaFvVuO3N6wjZLAUJS14H1pomOgTmPvUR7OeeM0CyeJhZPIwgnk1pXFl7FzPez+Eeq3LN+1JaIpERQRERERWPZf2LcdMKDQrPKxiV4yI6EJdcH7ymIGcXW9R8M2pR/7eJfiu64C/L0jtD8duv/CW2cxrk7lxyphpYZM5xS+1qxZQ5cuXUI+fuzYsWzYsKHMY0aNGlXR0EREREQ8X70A717tLYVw/uPeWDfM+6mlD2qOM+/zEvvCYhPhp3+GU34HW7+HCdfCo8fDxN9A2kzIzw8mVqlyahEsLAxmURo7dixdunShVatW1XpdgNzcXGJiVCVERERqDefg0z/D549B6nkw/GUvMeh1ddCRSVUoq+vqaX+EdbPh6//Asvdg8evelwHdLoFul0GznwQXu1Q6feovUIWzKOXm5jJixAgWLlxI586d+de//sWjjz7K5MmTycrKon///jz//POMHz+e+fPnM2LECBITE5kzZw7ffPMNt956K3v37iU+Pp5PPvkEgA0bNjBkyBBWr17Nz372Mx555BEA6tWrx6233sqUKVNITEzkvffeo0WLFqxZs4aRI0eydetWmjVrxquvvsqxxx7LVVddRUJCAosWLWLAgAE0aNCAtLQ0fvjhB9atW8fjjz/Ol19+yYcffkjr1q2ZPHkysbEaIyAiIhLx8nJg8m3eh/2TroJzH6u8JQokfJXWdTUqylsKpN1AOGcMrPzg/9m787goq/2B45/Djsoi4Aq4K+4LoqZluZWaS5q5lmmWWdlyuzcrfy232223bre0blnZZu6ZaVpWYqWmKYo7aGoq4IIbiLIz5/fHQUUFFQZ4Zobv+/Wa1zDPPM8zX0DG+T7nnO8XtsyB1W+biwW1I83U0ZZDoHJw+cYtSp3SWl99LycQFRWlY2JiLtoWFxdHs2bNzIPvn4Yj24o+QeIGyMu6fLu7N4R1KPyYmq2g72tXjGv//v3Ur1+f1atXc/311zNu3DiaN2/OuHHjCAoyi4FHjx7NsGHDGDBgAN26dePNN98kKiqK7OxsmjZtyty5c+nQoQOnT5+mUqVKzJw5kxdffJHY2Fi8vb2JiIhg9erVhIeHo5Ri8eLFDBgwgCeffBJ/f3+effZZBgwYwB133MGYMWOYMWMGixcvZtGiRYwdO5bjx4/z7bff4u7uzgsvvMDPP//MypUr2blzJ507d+brr7+mb9++DB48mDFjxjBo0KDLvs+LftZCCCGEcGzZZ2H+WPjzR+g2GW56Shqdi6KlHYVt82HrHPN52s0DGveGNsOhSR/w8LY6QpFPKbVRax11LfvKGsFzCksCr7S9GMLDw7n++usBuOuuu1i9ejUrV66kU6dOtGrViujoaHbs2HHZcbt27aJWrVp06GASUX9///NTN3v27ElAQAA+Pj40b96cAwcOAODl5UX//v0BaN++Pfv37wdg7dq1jBo1CjCJ5+rVq8+/ztChQ3F3dz//uG/fvnh6etKqVSvy8vLo06cPAK1atTp/PiGEEEI4qbMn4POBsOdnsx6w29OSBIor86sBXR6GB1bDA2vgugchaSPMuxvebALfPQ4J681UY+E0Ks74/1VG7oqsohQQDvcsteul1SVvrkopHnroIWJiYggPD+eFF14gMzOzWOf09r5w5cXd3Z3c3FwAPD09z79ewe1XUrly5ULP7ebmdtH53Nzcrul8QgghhHBQKQfhy9vN/bAvoVl/qyMSzqZmS6j5EvT6F+z7xUwd3TwbYmZAUAOzlrD1MAiqb3Wk4ipkRPCcoqoolUK55IMHD7J27VoAZs2axQ033ABASEgIZ86cYcGCBef39fPzIy0tDYCIiAgOHz7Mhg0bAEhLSytxItalSxfmzJkDwFdffUXXrl1L/P0IIYQQwgkd2Q4f3wxnk+HubyUJFPZxc4dGPWHIRzDpT7jtfVN85pdX4d22MKMvbPzM9DQUDsmSEUGlVB/gHcAd+FhrfdlwnVJqGPACppHNFq31qDINyp4GoFcRERHBe++9d3594IMPPsipU6do2bIlNWvWPD/1E2Ds2LE88MAD54vFzJ07l0ceeYSMjAx8fX35+eefSxTD1KlTueeee5gyZcr5YjFCCCGEqCD2r4bZo0yvuXt+gBrNrY5IuBJvP2h3p7mlJMC2eWakcMljsOxJaHqrKTLTsAe4S9FBR1HuxWKUUu7AbuBmIBHYAIzUWu8ssE9jYB7QQ2t9SilVXWudfKXzXrVYjChT8rMWQgghHNSORbBwPFStD6MXXr3puBClQWs4FGsSwu0LIP0EVAqBVkOhzQio1UbWppaB4hSLsWJEsCOwR2u9D0ApNQe4DdhZYJ/xwHta61MAV0sChRBCCCFEIdZ/BMsmmUbxI+dApSCrIxIVhVIQGmlut7xkihNtnQMxn8Af/4NqzUzV0VbDICDU6mgrJCsSwVCgYFWWRKDTJfs0AVBKrcFMH31Ba/1D+YQnhBBCCOHktIbol2DVmxBxK9wx4/JaCEKUFw8vMz206a2QcQp2fGNGCn9+AX7+FzS4yUwdbdrf9C4sg6Va4nKOWjXUA2gMdAPCgN+UUq201hetNlVK3Q/cD1CnTp1CT6S1vqxqpyhdrtKLUgghhHAJebnw3WMQOxMi74Z+b0ujeOE4fKtC1DhzO7EXts6DLbPhmwng5gU6z9zAVPRf8qj5WpLBUmdF1dAkILzA47D8bQUlAou11jla678wawobX3oirfV0rXWU1jqqWrVql72Qj48PJ06ckESlDGmtOXHiBD4+PlaHIoQQQojsdJh7p0kCb3wSBrwrSaBwXMENoftkeGyLKWLk4XkhCTwnJ8OMEIpSZ8U7wwagsVKqPiYBHAFcWhF0ETAS+FQpFYKZKrqvuC8UFhZGYmIix44dszNkcSU+Pj6EhcnCcyGEEMJS6Sdh1nBI3AD93oIO91kdkRDXRimo29lcyChMamL5xlNBlHsiqLXOVUo9DCzHrP+bobXeoZR6EYjRWi/Of+4WpdROIA+YpLU+UdzX8vT0pH59aWYphBBCCBeXkgAzb4dTB2DYF9B8oNURCVF8AWFmOuilvP3AZgM3aYFemsq9fURZKax9hBBCCCGEyzu6A2YOMaMpI2dDveutjkiIktk6z6wJzMm4sE25m+mizQbA4A9NL0xRpOK0j5C0WgghhBDCWe1fAzP6mq/HfS9JoHBurYeZda0B4YAy94M/gN6vQvxSmNFbpomWIlk9LIQQQgjhjHYuhq/vg6p14a6vIbDwCupCOJXWwwqvEBrSGBaMg+ndYcQsCO9Q/rG5GBkRFEIIIYRwNhs+hnl3Q63WMG65JIHC9TW+Ge77GbwqwWf9YMtcqyNyepIICiGEEEI4C60h+mVY+g9o0hvuXgyVgqyOSojyUS0Cxq+E8I7wzf2mGb3NZnVUTkumhgohhBBCOIO8XFj6d9j0ObS7C/q/Iz0CRcVTKQjuWgjfT4LV/4Hju00RGe8q5fLyi2KTmLJ8F4dSMqgd6Muk3hEMahdaLq9d2mREUAghhBDC0WWnw7zRJgm8cRIMnCZJoKi4PLyg/3+hz+uwa5kpIpNysMxfdlFsEpMXbiMpJQMNJKVkMHnhNhbFJpX5a5cFSQSFEEIIIRxZ+kn4chDs+h5ufRN6PGsacAtRkSkF1z0Ady4wfTQ/6gEH/yjTl5yyfBcZOXkXbcvIyWPK8l1l+rplRRJBIYQQQghHlZoIM/rAoVgY+hl0HG91REI4lkY9TREZbz/4vD9snl3qL5FwMp0Zq/8iKSWj0OcPFbHd0cmcAiGEEEIIR5QcB1/eDtlnzJqo+l2tjkgIx1StCdy3AuaPgUUPwLE46PlPcHMv0elsNs22pFR+2nmUn+OOEn8kDQAPN0WuTV+2f+1AX7vCt4okgkIIIYQQjubA7zB7BHj4wj3fQ82WVkckhGM7X0TmKVjzDhzbDUM+MiOF1yAzJ4+1+07w086jrIg7ytHTWbgp6FAviGf7NePm5jWIPZjC5IXbLpoe6uvpzqTeEWX1XZUpSQSFEEIIIRxJ3HemcXZgHRi9UHoECnGt3D2h/3+gejOTEH5yC4ycA1XrFrr7qbPZRMcn83PcUX7bfYyz2XlU8nLnpibVuLl5DbpHVKdqZa/z+9cNrgzgMlVDldaXD286o6ioKB0TE2N1GEIIIYQQJRczw/QIrB0Jo+ZB5WCrIxLCOe2Nhvljwc0Dhn8FdTsDcODEWX7aeZQfdx4lZv9JbBpq+HvTq1kNejWvQecGwfh4lmxKqSNQSm3UWkddy74yIiiEEEII17J1Hqx40RRaCQiDns9D62FWR3VlWsMvr8Gvr0HjW0xhGK/KVkclhPNq2APui0bPHo7+fAA/1HuKt4935M/kMwA0renHxO6NuLl5DVrWDsDNreJV4pVEUAghhBCuY+s8WPIo5ORX8UtNMI/BcZNBW55pFL/xM2h7Jwx4x0xxE0KUSGZOHmv2HOennen8kfIcL+ZM4dZ9L2GrMoRj/f6PXi1qEx5UyeowLSeJoBBCCCFcx4oXLySB5+RkwLInICsNfALA2x98/C/+2qtK+fXmKzhi6R8KlUPg8Ga44e9m9FJ6BApRbCfOZLEiPpmfdx5l1Z/HycjJo4q3BzdF1OFU01lkJb5L/00fw8Ez0OFjq8N1CJIICiGEEMK5aQ3JO2HHN2YEsDCZqWbUrSjKzVQX9AkA7wCTHBaWMF607ZL9PCtdPYm7dMTydKK5tR4Bvf5Zsu9fiApq37Ez51s8bDxwCpuGWgE+DI0Ko1ezGlzXIBgvj/y26ZFvQa3msGxSfhGZ2RBU39pvwGKSCAohhBDCOSXHm+RvxzdwfJdJ5ty9IS/r8n39w2D8Csg8DVmnTWKYmZr/9ekL9wW3nU6E5NQLz2vbleNx87gkYQy4PIlc/+HlI5YAB9aUzs9ECCe3KDapyKqceTZN7MFT/BR3lJ92HmXfsbMANK/lzyM9GnNz8xq0qO2PKuqCTId7IbgRzLsbPuoBw7+EejeU17fmcKRqqBBCCCGcx7HdF5K/Y3GAMh/kWgyCZgNh3y8Xj7gBePrCgHftWyOoNWSfLZBEFkgoL9t2uuhtFPW5S8ELKSWPTwgXsCg26bI+fT6ebtzZsQ6nM3OJjk/mxNlsPNwUnRsGn6/0GVrchu4n9po+nSf3Qb//QPsxpfydWEeqhgohhBDCdRzfAzu/gR2L4Oh2QEHdLnDrmyb586txYd9zyV5pVw1VCryrmJt/7ZKdw2aD/7aE00mXPxcQZl98QriAKct3XZQEAmTm2PhkzX78fDzoHlGdXs1r0C2iGv4+dhRUCm4I9/5k+nUueRSOxcPN/wb3ipUaVazvVgghhBDO4eS+CyN/R7aZbeHXQZ/XofnAKydjrYc5ZoVQNzfo9ULhI5Y9n7cqKiEcxqGUQqZN59v47M0X1vuVBt9A06vzx2dg3ftwfDfcMcNM564gJBEUQgghhGM4td+M+u34xlTRBAjrCL1fhea3QUCopeGVirIasRTCBdQO9CWpkGQwNNC3dJPAc9w9oO/rUK2pqSz8cS8YOceMGFYAskZQCCGEENZJOXgh+Tu0yWwLbQ8tBkPzQRAYbm18Qohysyg2iacXbiUz50JhJl9Pd169vdX5gjFl5q9VMG+0+XrYF1D/xrJ9vTIiawSFEEII4bhSEy8kf0n5F3Frt4ObXzTJX9W61sYnhLDEoHahbE44xWe/H0DBZVVDy1T9rjA+GmaNgC8Hw61TIGpc2b+uhSQRFEIIIUTZO30Idn5rkr+EP8y2Wm3Mmrnmgyp8Py8hhJGWmUeArycbn+2Fh3sZTAe9kqAGcN9PsOBe+O5x06Km9ysuW0TGru9KKRWstT5RWsEIIYQQwoWkHbmQ/B1ca7bVaAU9njNTPyvIOhwhxLWx2TS/7ErmpibVyj8JPMcnAEbNhZ+eh7XT4MSfcMenpriMi7E3vV2nlNoMfAp8r11lwaEQQgghSuZMcn7ytyi/SbqG6i2g+7Om119IY6sjFEI4qC2JKZw4m03PZtWtDcTNHXq/DNUi4Lu/myIyo+a63MUrexPBJkAvYBzwrlJqHvCZ1nq33ZEJIYQQFdXWeY5dVfLS+G543Gzf8Y1J/rQNQiKg29Nm2mf1ptbGK4RwCivjk3FTcFOTalaHYkTeDUENYe5d8FEPGPY5NOhmdVSlptSqhiqlugMzgcrAFuBprfXaUjn5NZCqoUIIIVzC1nmF95kb8G75JYM2G9hyIC8n/z73wuO4xRD9EuRmXn5ccGNoebuZ9lm9WfnEKoRwGf2nrsLHw50FD3axOpSLndpvisgc323eh/evdtgLdeVWNVQpFQzcBYwGjgKPAIuBtsB8QFZ+CyGEEMWx4sWLk0Awj5c9ASf/KpCg5RZI1Ap5XFgSd9nj3MK367zix12lBjy8AZQqnZ+DEKJCOXo6k+1Jp3myT4TVoVyuaj2490f49FbYMvvC9tQEc+EOHCoZvFb2Tg1dC3wJDNJaJxbYHqOU+sDOcwshhBCuL/0kJG0yPfSSNpkPFoXJTIVfXgEUuHuCm6epZOfmWcRjD3D3Ml97+IC3X/GOcfMosE+Bx99OLDy+M8mSBAohSmxlfDIAPZpavD6wKD7+kHnq8u05GeYCXgVMBCOKKhCjtX7dznMLIYQQriXrDBzeciHpO7TJTDk6J6QJeFaCnPTLj/UPg79tNUUMrPTLa4UnqwFh5R+LKBOLYpOYsnwXh1IyyrePm6jQVsQnExroS0QNP6tDKVpqUhHbEwvf7uDsTQR/VEoN1VqnACilqgJztNa9r3SQUqoP8A7gDnystX7tkufHAlOAcz/taVrrj+2MVQghhCg/udmQvAOSNkJSrEn6jsWbQioAAeGmiXr7sVA7Emq3NWXLi1oj2Ouf1ieBYNbDFBZfz+eti0mUmkWxSUxeuI2MHDM9OCklg8kLtwFIMijKTFZuHmv2HOf2yFCUI88sCAhzqQth9iaC1c4lgQBa61NKqSuO5yql3IH3gJuBRGCDUmqx1nrnJbvO1Vo/bGd8QgghRNmz2UyvqaRNJvE7tAmObIO8bPO8bxCEtodmA0zSFxoJVYr47/Lc9CJHrRrq6PEJu0xZvut8EnhORk4eU5bvkkRQlJk/9p0kPTvPcaeFnuNiF8LsTQTzlFJ1tNYHAZRSdYGrlSHtCOzRWu/LP2YOcBtwaSIohBBCOB6tzRXh80lfLBzaDNlp5nnPymakr9OEC0lfYN3irZ9rPcyxEytHj0+UyKmz2SSlZBT63KEitgtRGqLjk/HxdKNLwxCrQ7kyF7sQZm8i+AywWin1K6CArsD9VzkmFCg4ppoIdCpkvyFKqRuB3cDjWusiVs8LIYQQZejs8QLFXDaar9OPm+fcPKFmK2gz/ELSF9LEMaZwCnENtNas23eSORsO8v32I0XuVzvQtxyjEhWJ1poV8Ufp0jAEH08neO90oQthdiWCWusflFKRwHX5m/6mtT5uf1gsAWZrrbOUUhOAz4Eel+6klLqf/MSzTp06pfCyQgghKoSiGrZnpZnRvXPFXJI2QerB/IMUVIuAJr3NiF9oJNRoCR7eln4rQpTE8TNZfL0xkbkbEth3/Cx+Ph6M7BBOdX8fpkXvuWh6qJuCf9zcxMJohSvbe+wMCSczmHBjQ6tDqXDsHREEyAOSAR+guVIKrfVvV9g/CQgv8DiMC0VhANBanyjw8GPgjcJOpLWeDkwH01C++KELIYSocC4txpKaAN88AD89D2lHOL/CIbCOSfY6jjf3tdqYFgxCOCmbTbNm73HmrE/gx51HyMnTdKhXlYndG3Frq1r4epnRmNBA3/NVQwMqeZKSnsP+k4VUshWiFETnt43o7ujrA12QvQ3l7wMewyRzmzEjg2spZPSugA1AY6VUfUwCOAIYdcl5a2mtD+c/HAjE2ROnEEIIcV5hDdt1HmScgm6TTdJXux1UdvC1KkJco+TTmczfmMicDQdJOJlB1Uqe3N25HiM6hNO4kFL9g9qFXlQY5h/ztjA1+k861KtK18bVyjN0UQFExyfTtKYfoTL9uNzZOyL4GNABWKe17q6Uagq8cqUDtNa5SqmHgeWY9hEztNY7lFIvAjFa68XAo0qpgUAucBIYa2ecQgghhFFUv6fcLOj2VPnGIkQZybNpftt9jFnrDxIdn0yeTdO5QTBP3BJB7xY1i7UW69+DWrA1MYW/zdnMsse6UsPfpwwjFxVJakYOG/afYsKNDawOpUKyNxHM1FpnKqVQSnlrreOVUhFXO0hrvQxYdsm25wt8PRmYbGdsQgghxMX+mE6Rxa2dtA+UEAUdSslgXkwC8zYkcCg1k5AqXtzXtT4jOtShfkjlEp2zkpcH798ZycBpa3hkdiyz7uuEh7tbKUcuKqJVfx4jz6Ydv22Ei7I3EUxUSgUCi4CflFKngAP2hyWEEEKUorwc+OFp2PAx1GwDx3dDrmv0gRIiJ89GdHwyc9Yf5Nfdx9DADY1CeK5/c3o2q4GXh/1JW+Mafrw8uCV/n7eFt3/ezaTeTe0PXFR40XHJBFbypF2dqlaHUiHZWzV0cP6XLyilVgIBwA92RyWEEFYrqqqkcD4ZKTB/LOxbCV0ehV4vwPav5fcrnF7CyXTmbDjI/JhEktOyqO7nzcTujRgWFU54UKVSf73bI8P4Y99J3lu5lw71gugWIaM4ouTybJpfdh+jW5NquLsVo8+qKDUlTgSVUu7ADq11UwCt9a+lFpUQQlipsKqSSx41X0uy4FxO7oNZw839wGkQOdpsd6E+UKJiyc618dPOo8zZcJBVfx7HTUH3iOqM6FiH7hHVynzK5r9ua8GWxBQen2vWC9YKkAIfomS2JKZw8mw2PZrVsDqUCqvEiaDWOk8ptUspVUdrffDqRwghhJMorKpkTgb8+By0GgpKrlw6hf1rYO5dgIbRi6B+V6sjEqLE9h07w9wNCSzYmMiJs9mEBvryeK8mDOsQVq7JmI+nO+/dGcnAqat5ZFYss++/Dk9ZLyhKIDouGXc3xU1SidYy9q4RrArsUEqtB86e26i1HmjneYUQwjpFVZU8cwT+0wwadIMG3aHBTeBXszwjE9cqdiYs+RtUrQej5kKwNCoWziczJ4/lO44we/1B1u07ibubolczM/p3Y2PrptM1rFaFV25vxWNzNvPmj7uY3LeZJXEI5xYdn0z7OlUJqORpdSgVlr2J4HOlEoUQQjiKPSsosqqkb1Wocx3sXg5bZptt1ZtfSAzrdgHvKuUUqCiUzQYrXoA175jfy9DPzO9NCCfy59E0Zq9PYGFsIinpOdQJqsSk3hEMbR9GdQdp3XBb21D++OskH/66j471gugp0/tEMRxOzWDn4dM83VeKDlnJ3mIxsi5QCEclxU6Kb9cPMG80+IdB+onLq0r2fcP8DG02OLIV9v1ibjEzYN374OYBYR2hYXeThNSOBHd7r7eJa5Z1BhbeD7uWQtQ48/tylyvNwvEsik1iyvJdHErJoHagL5N6m95+S7cdZvb6g2w8cApPd8UtLWoyskMdujQMxs0Bi2k83785mw+m8I/5W1j6aFdpCC6u2cr4YwDSNsJiSusirnxfy8FKpXHh0rkX4Amc1Vr7l0JsxRIVFaVjYmLK+2WFcEyXFjsBk8gMeFeSwaLELYH590DNlnDXQtjz87Un0jkZkPAH7F1pEsPDWwAN3v5Qr6tJCht2h+BGsr6wrKQmwuwRcHQH9H4VOk2Qn7VwSItik5i8cBsZOXnnt7m7KTzcICtX0yCkMiM71uH2yFCCq3hbGOm12X/8LP2nrqZxjSrMvb9zqbSqEK7vvs9jiD9ymlVPdkfJe3WpUkpt1FpHXcu+9o4I+hV4UQXcBlxnzzmFEHbSGn56vvBiJytelESwMNu/hq/HQ2h7uGsB+AQUr6qkp2/+9NBu5vHZE7D/t/zEcKUZoQIz0nhuvwY3QRW5EloqkjbC7JGQnQ6j5kHjm62OSIgiTVm+66IkEEwZfS93d+be34GO9YOc6oNxvZDKvD6kNRNnbeKNH+J5tn9zq0MSDi4zJ481e44zNCrMqf6tu6JSm7OkzdDiIqXUP4GnS+u8QoirOH0IkjbBoU3597GQmVL4vkUVQanINs+Gbx+COp1NURFvv6sfczWVg6HFYHMDOM2LJRgAACAASURBVPmXSQj3/QLx38HmmWZ7jZYF1hd2Bq/K9r92RbN9ISx60CTVoxdBDfkQKhzboZSMQrdn5uTRqUFwOUdTOvq1rsUff9Xl49V/0bF+ELe0kCJaomjr9p0gIyeP7jIt1HJ2JYJKqdsLPHQDooBMuyIS5cfR15A5enxWSD9pEr1DmyAp/z7tsHlOuZsPwc1vg7jFkHHq8uMDwso3Xke38TNTWbLBTTBiNniVfgNmAILqm1vUOLDlmamj5xLD9dNh7TRw84TwTtCwm0kMa7cDN/eyiccVaA2/TYGVL5uf2/CvoIqUIBeOr2aAD4dTL/+oVNvJ19c9068ZsQdTeGL+FpbW8i+ThvbCNUTHJ+Pr6U5nJ73w4UrsHREcUODrXGA/ZnqocHSO3jDb0eMrD9npJmE4P9K3yTTFPie4kVl/FtoeQiOhZiszRRGg3g2XrxEEMzUxJxM8HaPqnKX+mA7fT4LGt8CwL8vvZ+Lmbn5foZHQ9R/m93xwbX7hmZUQ/ZK5+QSY32/D7iYxDGpw8Zq3inyhJCcTFj8M2+ZD6+Fm7av8mxZOIM+mCfT1vCwR9PV0Z1LvCIuiKh3eHu68NyqSflNX8fCsTcx/oIusFxSX0VoTHZ/M9Y2C8fGUi51Ws6tYjCORYjHF9HZLk1xdytMXGvcGnWeuuNvyQNvyH9sKPLYV8ZwuZN9zj21Fn+vS/W25hcft5Qc9njEffAPCza1SkPMXhcjLMUUuCk7vTI4zPwsA/1AzQnQu6avVFnwDr3zOSxOF2pEQ962pajniq4q9Pu33qfDjs9C0P9wxAzwcqCDD2eMXqpHu++XC32lAHTNy2aAbZKbCj89UzGJAZ5Jhzp2QuB56PGeSaWf/+xcVxpvLdzFt5R7uaB/G2r0nLqoaOqhdqNXhlYofth/hgZkbGdulHi8MbGF1OMLB/Hk0jZvf/o1XBrdiVKc6VofjkopTLMbeqqGfA49prVPyH1cF3tJajyvxSUtIEsFiyDgFr9cr+vmQJmaaoXIDNzdzr9zNSMa5ry99TrkVeN6twOMrPXfpserC49X/ufbvx7NSfmJYIDkMDL+wzT/UsUrI22xwcq8pcHFupO/INsjNv0LsW9UkbaGRJvGrHQl+pdSfacci+OYBqBQMI2dDrdalc15n8tsUM+LWYjDc/pFj/du4lNZmFHjfSlN45q9VkJVa9P4B4fD49vKLr7wd3QGzhptkefAH0GKQ1REJcc2+33aYB7/axPCocF4b0sqli2T8a8kOPl2zn//dGUnfVrWsDkc4kA9+3ctr38ezdnIPagU493RoR1VuVUOB1ueSQACt9SmlVDs7zynKSl4ubPoMol8uep+AcHh4Q7mFVKRt8wsfsQwIh/t/hdSDZqQrNRFSEsy+qQkmoTp77JKDFPjVujg5vDRh9AkofozXMjVPaziddHHSd2gzZJ02z3tWhlptoMN9JvGrHQlV65XdCEeLQeb8s0fCjN5w+3RoNuCqh7kErc16st+mQOsRcNt7jt/jTykIbmhuHe4zf8OHN8PHPQvf35WLAe1eDgvGmWI+9ywzfy9COIldR9L4x/wttA0P5MVBLVw6CQSY3LcZmw6m8OSCrTSv7U/dYCmEJYzo+GSa1fKXJNBB2PspyE0pVVVrfQpAKRVUCucUZWHfL/DDZEjeCXVvMOuOVr15+dSyns9bFuJFej5feB+8ns+bioyVg81UycLkZEBq0oXk8HzCeNAkY3FLIC/74mO8/fOTw7ACCWP4hW1+NS8u3FHUGsbsM2YKX9LGC9M8zyabfdw8oUYLaHXHhZG+ahHlXxCkdlu4fyXMGQVz76oY0+vOtdT4/V2IvBv6/9c5C7G4e0BYlPl3WdiFEqVg5SvQ/h7wd5Gr8FrDuvfNVN6arWDkHPCvbXVUQlyz1PQc7v8yhsreHnxwV3u8PZzwvaeYvDzcmDayHf3eXcXEWZtY8EAXWQ8mSE3PYeOBUzx4U0OrQxH57E3a3gLWKqXm5z8eClxhuEmUuxN74cfnTB+zwDow7AtoNtB8YAys47jFJs7FUZL4PH0hpJG5FcZmM8nZueQwNbFAwphgGoNf2n7BzcN8+AyoY2LZtazwPn3fPZ7/QJkpto165k/zbG+SQEcpaOFXE8YuhcWPQPS/4dguGDjVceIrTVrD90/B+g+hw3jo+4aZ1uzMCrtQ4u4NIY3h1zdg1VumemzHCRDe0XmT/LwcWPaEqe7atL8ZwZYWG8KJ5Nk0j86J5VBKBnPuv46aAS74HluE8KBKvDWsLeO/iOHlpXH8e1BLq0MSFvv1z2Pk2bS0jXAgdheLUUo1B3rkP4zWWu+0O6oSkDWCl8g8babArfufKYTR9e9w3UTX/KBfFrLSLowkpibkTz+9JGEsypjvzHRPH//yi7ektDZJQ/S/ITQKRswqvfWIjsBmg6WPm0Si88Nwy0vOmxRdqqipySf2woZPIHamWU9Yq41JCFsOca6///STMH8M/PUb3PB3M3Lt7Am8qHDe+CGe93/Zy8uDW3Jnp7pWh2OJl5fu5KNVfzF1ZDsGtJHR/Irs8bmb+XX3MTY80wt3Nxf5v9gBlWexmOuAHVrrtPzH/kAzrfUfJT5pCUkimM+WZz4ARv/bFFRoeyf0fM6MAInS83aLwtdjOWuxjrglsPB+U6hm5GyTPDg7Wx58+zBsmWWmvvZ4znWSwGuRdQa2zjV9Co/FmwJBkWOgw72O30/y+B6YNcyM2A+cCm1HWh2REMW2dOthJs7axMiO4bx6ewUszJUvJ8/G8A/XsvvoGZY8cgP1Q2RUvyLKs2miXvqJ7hHV+c/wtlaH49KKkwjae3n1f8CZAo/P5G8TVti/BqbfZKaMBTU068AGvSdJYFno+c8LPfvOcaQ1lsXVbACMWw4omNEHdn5rdUT2ycs1ie2WWdD9GfN7qUhJIIB3FZP0PbQO7l4MdTrDmv/Cf1vD3NGwf7UZEXY0f/1miuFkpsCYJZIECqcUf+Q0T8zfQmSdwArfQsHT3Y1poyLxcFc89NUmMnPyrA5JWGBzwilOpefItFAHY28iqHSBIUWttQ0pFlP+Tu2HeXfDZ7dCRorpizbuh6KLqQj7tR5merYFhAPK3Dt7D7darWF8tFnLOO9us9bMEROFq8nNhgX3wPYF0OsFuOlJqyOyllKm/+CIr+DRzdDlYdi/Cj7rB/+7HmI+NU3tHcHGz+DLwebi1X0roG5nqyMSothS0rO5/4uN+PlUnOIwV1M70Je3h7Ul7vBp/rXEkhVEwmIr4pJxd1Pc2KSa1aGIAuydGroQ+IULo4APAd211uXe3KlCTg3NSoNV/4G175kKiDc8Dl0euXykSojiyMk0o8pb55p1Zbe95zz/pnIyzbqy3T9An9fgugetjsgxZaebRPmP6XB0m2mf0m40dBxv2ouUN1ueqeq6dho07AlDPy1ZSxchLJZn04z9dD3r9p1gzv2daV+3qtUhOZTXvo/ng1/38s6IttzWNtTqcEQ56vPf3wjw9WTuBLnAV9bKc2roA0AXIAlIBDoB99t5TnE1NhvEfgVT25vG6y0GwcMxZuTDWT6wC8fl6QODPzTTX7cvhE9vhdOHrY7q6rLTYc5IkwT2+48kgVfiVcm00XhgFdzzPTTobgpLvdMWZo2AvdHlNxqclWZamaydZorajJonSaBwWm8sj2fVn8d58baWkgQW4olbmtChXlUmL9zGnuQzVz9AuIRDKRnEH0mjZzOZFupo7K4a6igqzIjgwXXww9NwKNa0JOjzOoR3sDoq4aril8LX400F1BGzHLeJd9YZmD3CrHu7bRq0u8vqiJxPahLEzDDTM9OPm/YnHe+HNiNME/eykHLQJJ7H4qHv62ZEUggntWTLIR6ZHcudnerw8uBWVofjsI6kZnLru6uoVsWbRROvx9dLps66upnrDvDsou38/PcbaVS9jP4/EeeVZ9VQH+BeoAVwvi651npciU9aQi6fCKYkwM//hO1fg18t6PUvaDVUyqmLsndku0myzh6HQe9Dy9utjuhimafhq6GQuMGMZLYeanVEzi0nE3Z8Y/ouHooFb39oO8okhcGl2AQ4YYMZwc3NhmGfQcMeVz1ECEe189Bphvzvd1rU9mfW+Ovw8pD/m6/k193HGPvpeoa2D+ONO1ygSrW4ons/28Du5DR+m9QdVdEKt1mgPKeGfgnUBHoDvwJhQJqd5xQFZZ+Fla/AtA5mdObGJ+GRjdBmuCSBonzUbAnjV5qWEgvugV9ec5wiMhmn4MtBkBRjiiRJEmg/Tx9TqXP8Srj3Z2jS2/QlnBoJM4fA7h/N9HR7bFtgitV4VYb7fpIkUDi1U2ezmTAzBn9fD96/K1KSwGtwU5NqTOzWiHkxiXy9sZBWTMJlZObksWbvcXo2rSFJoAOy992qkdb6OeCs1vpzoB9mnaCwl9amYfTUKPj1dYjoCw9vgB7PmA9PQpSnKtVgzGJoMwp+edUkhFZXmjx7Aj4fCEe2wfCZZq2sKD1KmWnnQz6Gx3dAt/8zP+tZQ2Fae1j7PmSmFu+cWpsLW1/fC2FRcF80VIsom/iFKAe5eTYemR3L0dQsPrirPdX9fK5+kADgb70a06l+EM8u2s6fR2UMwVWt3XuCzBybtI1wUPYmgjn59ylKqZZAACC/aXslboRPboaF46FKdbjnB1NFL7CO1ZGJiszD20wNvfnfsGMRfNoXTh+yJpYzyfB5fzi+G0bMNhdKRNnxqwHdnoK/bYchn0DlarB8MrzVDL77OyTHX/0cORnmAsKvr0Pbu2D0IqgcXPaxC1GG3li+i9V7jvPSoJa0qyPFYYrDw92NqSPbUdnbnYe+2kR6dq7VIYkyEB2fTCUvdzrVD7I6FFEIe3v+TVdKVQWeBRYDVYDn7I6qojp9CH7+F2ydA1VqmLL9bUbJFFDhOJSC6x81hUS+vhemd4eRs0zhovJy+pAZCTydZCpMNrip/F67ovPwglZ3mNuhzbB+OsTOhJhPoP5N0GkCNOlj2tlsnQcrXoTURLOu2d0LUg6Y9c3XP2b+LQnhxL7dnMT03/Yx+rq6DOsQbnU4Tqm6vw/vjGjHXZ/8wbOLtvPW0DYyfdCFaK2Jjk/m+kYh+HhKUSBHJFVDHUFOBvw+zbSCsOVC54nQ9R9lV6lPiNJwdCfMHm5G5257zyQHZS0lAT4fYArX3DlfGo47grPHYdPnZh3h6SQzcyGsA8Qvg9yMi/e97iHo86o1cQpRinYcSmXI/36nVWgAX90nxWHs9fZPu3lnxZ+8MaS1JNUuZNeRNHr/9zdevb0VIzvKrLbyUp7FYoQ9tDZ92qZ1hJUvQaOeMHE99HpBkkDh+Go0NwVFakea0cHol+wvInIlJ/8yPQ3TT8LdiyQJdBSVQ8yFq8e2wrAvIKCOqW58aRIIELek/OMTopSdPJvN/V9sJNDXi/fvbC9JYCl4tGdjrm8UzHPfbif+yGmrwxGlZEX8UQC6R8iqMUdlybuXUqqPUmqXUmqPUurpK+w3RCmllVLXlNU6lUObzYfaBfeY5sljvjMFL4LqWx2ZENeucgjc/a3p2/fbFJg/xlS6LW3H95i/l+w0GPOtKTQiHIu7BzS/De5ZChQxtStVqgMK55abZ+PhWZs4diaLD0e3p5qft9UhuQR3N8V/h7fD39eTh77axJksWS/oClbGJ9Oitj81A6SIkqMq90RQKeUOvAf0BZoDI5VSzQvZzw94DPijfCMsY2lH4duJML2bKXTR/78w4Veo39XqyIQoGQ8vGDgNbnkZ4r+DGX1K9wN/crwpTJOXbS6Y1G5XeucWZSMgrHjbhXASr30fz+97T/DyoJa0CQ+0OhyXUs3Pm3dHtGP/8bM88802XGXpUkWVkp7NxgOn6CnVQh2aXcVilFKFdZZOBbZprZOLOKwjsEdrvS//HHOA24Cdl+z3b+B1YJI9MVqmYKGEgDDo/n9w5ij89hbkZkKXh+HGSWY0UAhnp5T5Nx3SBBaMg496wIhZ9o/cHdkGX9wGbp4w9jtpNeAsej4PSx4165/P8fQ124VwUt/EJvLx6r8Y07kuQ6NkHVtZ6NwwmMd7NeGtn3bTqX4wozrJujJn9evuY9g00jbCwdk7Ingv8DFwZ/7tI+ApYI1SanQRx4QCCQUeJ+ZvO08pFQmEa62XXunFlVL3K6VilFIxx44dK+G3UAa2zjMfglITAG3uFz0EP79gRv4m/gG3vCRJoHA9TW6B+342H/o/vRW2zi/5uZI2wWf9wcMH7lkmSaAzaT0MBrwLAeGAMvcD3jXbhXBC25NSefrrbXSsH8Sz/S+bxCRK0cTujejaOIQXluxgx6Fi9ioVDiM6Ppngyl60CZORc0dmbyLoATTTWg/RWg/BTPXUmKbyT5XkhEopN+A/wD+utq/WerrWOkprHVWtWrWSvFzZWPHixVfCAdCm99bI2RDc0JKwhCgX1ZuaRuFhHWDhfebvobhFZBLWm5FAH3+TBMrfjPNpPQwe3w4vpJh7SQKFkzpxJosJX24kqLIX798Ziae7FIcpS25uiv8Ob0vVSp5M/GoTaZk5Vz9IOJTcPBu/7DpGt4jquLlJOxBHZu+7WbjW+miBx8n5205yodn8pZKAgnMqwvK3neMHtAR+UUrtB64DFjtVwZii1kedPV6+cQhhlcrBMPobiBwDq96CeaMh68y1Hbt/DXw52BSiued7qFqvTEMVQoii5OTZeHhW7PniMCFVpDhMeQiu4s3UkZEknMrg6YWyXtDZxCakkJqRQw+ZFurw7E0Ef1FKfaeUGqOUGgN8m7+tMpBSxDEbgMZKqfpKKS9gBKYZPQBa61StdYjWup7Wuh6wDhiotXaeJoFSKEEIU0RmwDvQ53XYtcwUkUlJuPIxe1fCzCHgH2qSQPmbEUJY6JVlcazdd4JXB7eitUxxK1cd6wfxj1uasHTrYWauO2B1OKIYouOT8XBTdG0SYnUo4irsTQQnAp8BbfNvXwATtdZntdbdCztAa50LPAwsB+KAeVrrHUqpF5VSA+2MxzH0fN6skSpICiWIikgpuO4BGDUfUg7AR93NtM/C7P4RZg2HoAYwdin41SzfWIUQooCFmxL5dM1+xnapx5D2clHKCg/c2JDuEdX493dxbEuU9YLOIjoumQ71gvD38bQ6FHEVylWG26OionRMjAMNGl5aNbTn87JGRlRsx3aZRO90EgycCsrtwt9IpWDTKL5WKxi9CCoFWR2tEKIC25aYyh0f/E67OoF8eW8nWRdooZNns+n37io83d347tEbJLlwcImn0rnh9ZU8c2szxt/YwOpwKiSl1Eat9TUtqSuN9hGvA9UxHYQVoLXW/vac1yW0HiaJnxAFVYuA8dEw7274ZgK4eYAtv2lw+nGTGLYfJ0mgEMJSx89kMeHLGEKqePPeKCkOY7Wgyl5MG9WO4R+u48n5W/nfXZEoJQVIHNXKeNM9rkczWR/oDOx9d3sDs34vQGvtr7X2kyRQCFGkSkGmiIxX5QtJ4DnaBqvetCYuIYTAFId56KtNnDibzYej2xMsxWEcQvu6QTzZJ4Ifdhzhs9/3Wx2OuILo+GTqBleiQUhlq0MR18DeRPCo1jquVCIRQlQM7p6QnV74c0VV3BVCiHLw8tI41v91kteGtKJlqPT6dSTjuzagV7PqvLIsjs0JRdUjFFbKyM7j970n6NG0uozaOgm7poYCMUqpucAiIOvcRq31QjvPK4RwZQFhkFpIBVGpEiqEsMj8mAQ++30/995Qn8Ht5L3I0SileHNoG/q9u5qxM9bj6+XOkdRMagf6Mql3BIPahVodYoX3+97jZOXapG2EE7F3RNAfSAduAQbk3/rbG5QQwsVJZV0hhAPZkpDCM4u206VhMJP7NrU6HFGEwEpeDI0KIyUjh8OpmWggKSWDyQu3sSg26arHi7IVHZ9MJS93OtaXtf7Owq4RQa31PaUViBCiAjlXSEkq6wohLHYsLYsJX26kWhVvpo2KxEOKwzi0+TGXLyHIyMljyvJdMipoIa010fHJdG0cgreHu9XhiGtUokRQKfWk1voNpdRU4LL+E1rrR+2OTAjh2qSyrhDCYtm5NiZ+tYmUjGwWPNCFoMpeVockruJQSkaxtovyEX8kjcOpmfytV2OrQxHFUNIRwXMFYhyocZ8QQgghxLV7aelO1u8/yTsj2kpxGCdRO9CXpEKSvtqBvoXsLcpLdH7biO4Rsj7QmZQoEdRaL8m//7x0wxFCCCGEKHvzNiTwxdoDjO9an9vaypRCZzGpdwSTF24jIyfv/DYvDzcm9Y6wMCoRHZ9Mq9AAqvv7WB2KKAZ7G8o3AZ4A6hU8l9a6h31hCSGEEEKUjdiDp3h20XaubxTMU32kOIwzObcOcMryXRxKyUApqOHnzcA2tS2OrOI6eTab2IOneLiHTAt1Nva2j5gPfAB8DORdZV8hhBBCCEslp2XywMyNVPf3ZtpIKQ7jjAa1Cz2fEC7YmMgT87eweMshKRZjkV93J2PT0FPaRjgde9/9crXW/9Nar9dabzx3K5XIhBBCCCFKUXaujYdmbiI1I4fpo6OoKsVhnN7t7UJpGerPGz/Ek5kjYxJWiI4/RkgVb1rJOlunY28iuEQp9ZBSqpZSKujcrVQiE0IIIYQoRS9+t4OYA6d44442NK/tb3U4ohS4uSmeubU5h1Iz+WT1X1aHU+Hk5tn4dVcy3SOq4eamrA5HFJO9U0PH5N9PKrBNAw3sPK8QQgghRKmZs/4gM9cdZMKNDWQ9mYvp3DCYm5vX4P2VexgaFUZ1PylYUl42HjjF6cxcesi0UKdk14ig1rp+ITdJAoUQQgjhMDYdPMXz3+6ga+MQnpTiMC5pct+mZOXaePunP60OpUKJ3pWMp7vihsYhVociSqCkDeV7aK2jlVK3F/a81nqhfWEJIYQQQtgv+XQmD3y5kZoBPkwd2Q53mb7mkhpUq8LoznX5/Pf9jOlSl6Y1ZepveYiOS6Zj/SD8fDytDkWUQEmnht4ERAMDCnlOA5IICiGEEMISi2KTzrcX8HBXoGHJozcQWEmKw7iyx3o25uuNiby8NI4v7+1kdTguL+FkOn8mn2F4h3CrQxElVNKG8v/Mv7+ndMMRQgghhCi5RbFJFzUcz8nTeLor4g+nySiRiwus5MWjPRvz0tI4ftmVTLcIWbdWllbuSgagZ7MaFkciSsru5jlKqX5KqSeVUs+fu5VGYEIIIYQQxTVl+a7zSeA5OXmaKct3WRSRKE93d65HveBKvLw0jtw8m9XhuLQVccnUD6lM/ZDKVociSsiuRFAp9QEwHHgEUMBQoG4pxCWEEEIIUWyHUjKKtV24Fi8PN57u25Q/k88wZ0OC1eG4rPTsXNbuOyHVQp2cvSOCXbTWdwOntNb/AjoDTewPSwghhBCieLJy8/DxdC/0udqBvuUcjbBK7xY16VgviLd/2k1aZo7V4bikNXtOkJ1rk0TQydmbCGbm36crpWoDOUAtO88phBBCCFEsqek5jJmxnoycPDwuqQzq6+nOpN4RFkUmyptSimf7N+PE2Wze/2Wv1eG4pOj4ZKp4e9ChXpDVoQg72JsILlFKBQJTgE3AfmCWvUEJIYQQQlyrhJPpDPngdzYeOMXbw9vw5tA2hAb6ooDQQF9evb0Vg9qFWh2mKEetwwIZ3C6UT1b/RcLJdKvDcSlaa1bGJ9O1cQheHnaXGxEWKmn7CJRSbsAKrXUK8LVS6jvAR2udWmrRCSGEEEJcwdbEFMZ9FkN2bh5fjOtE54bBAJL4CSb1jmDZtsNMWb6Ld0e2szocl7Hz8GmOnM6ku0wLdXolTuO11jbgvQKPsyQJFEIIIUR5WRF3lOEfrsPbw42vH+xyPgkUAsy60PtvbMDiLYfYdPCU1eG4jJXxpm1Ed2nP4fTsHc9doZQaopRSV99VCCGEEKJ0fLl2P+O/iKFR9Sp8M7ELjWv4WR2ScEATbmpISBVvXvpuJ1prq8NxCSvik2kTFkA1P2+rQxF2sjcRnADMB7KUUqeVUmlKqdOlEJcQQgghxGVsNs0ry+J47tsd9GhanbkTrqO6n4/VYQkHVcXbgyduacKmgyks23bE6nCc3okzWWxOSJFpoS7CrkRQa+2ntXbTWntprf3zH/uXVnBCCCGEEOdk5uTx8OxNTP9tH3d3rsuHo6Oo5FXicgeighgaFU7Tmn689kMcmTl5Vofj1H7dfQytoWfTGlaHIkqBvQ3lV1zLNiGEEEIIe5w8m82dH//Bsm1HeObWZvxrYAvc3WRlirg6dzfFM/2akXAyg89/3291OE5tRXwy1fy8aVFbxn1cQYkSQaWUj1IqCAhRSlVVSgXl3+oBUqZLCCGEEKVm//Gz3P7+GrYlpfLeqEjG39gAKU8giqNr42p0j6jGtOg9nDiTZXU4Tiknz8Zvu4/RI6I6bnIRxiWUdERwArARaJp/f+72LTDtagcrpfoopXYppfYopZ4u5PkHlFLblFKblVKrlVLNSxinEEIIIZzYxgOnGPz+GlIzcph1Xyf6ta5ldUjCSf3frc1Iz8njnRV/Wh2KU4rZf4q0zFxZH+hCSpQIaq3f0VrXB57QWjfQWtfPv7XRWl8xEVRKuWPaTvQFmgMjC0n0ZmmtW2mt2wJvAP8pSZxCCCGEcF7fbzvMqI/W4e/rycKHrieqXpDVIQkn1riGH6M61uGrPw6yJznN6nCczspdyXi6K25oHGJ1KKKU2FssZmoJDusI7NFa79NaZwNzgNsuOW/ByqOVAan3K4QQQlQQWms+XrWPh2ZtokVtfxY+2IX6IZWtDku4gL/1akwlT3deXRZvdShOJzo+mesaBFPFWwo0uQp720eURCiQUOBxIoWsK1RKTVRK7cWMCD5aTrEJIYQQwkJ5Ns0Li3fw0tI4+rSoyazx1xFcRfqVidIRXMWbiT0asSI+mdV/Hrc6HKdx8EQ6e5LPSBN5F2NFInhNtNbvaa0bAk8B2sg0zgAAIABJREFUzxa2j1LqfqVUjFIq5tixY+UboBBCCCFKVXp2LhO+3Mjnaw8wvmt93hsViY+nu9VhCRcztks9wqr68tLSneTZZNLZtYiOPwpAz2aSCLoSK9pHJAHhBR6H5W8ryhxgUGFPaK2na62jtNZR1apVu1q4QgghhHBQyWmZjJi+juj4o7x4Wwue6ddcKhOKMuHj6c5TfZoSfySNBRsTrn6AYEV8Mg2qVaZusEzRdiVWtI/YADRWStVXSnkBI4DFl5y/cYGH/QAp7ySEEEK4qD3Jadz+/u/8efQM00dHcXfnelaHJFxc/9a1aFcnkDd/3M3ZrFyrw3FoZ7Ny+WPfSXrItFCXU+7tI7TWucDDwHIgDpintd6hlHpRKTUwf7eHlVI7lFKbgb8DY0oYpxBCCCEc2Lp9J7j9/d/JzLExd8J19Gpew+qQRAWglOK5/s05lpbFh7/utToch7Zmz3Gy82z0kGmhLqdEZX+01u8A7yilHilJ5VCt9TJg2SXbni/w9WMliUsIIYQQzmNRbBKTFmyhTlAlPrunI+FBlawOSVQgkXWq0r91Laav2sfITnWoFeBrdUgOKTo+GT9vDzpI+xaXY2+xmCNKKT8ApdSzSqmFSqnIUohLCCGEEC5Ka817K/fwt7mbiaxTlYUPXi9JoLDEU32aYtMwZfkuq0NxSFprouOTubFJNTzdHbbGpCghe3+jz2mt05RSNwC9gE+A/9kflhBCCCFcUU6ejckLtzFl+S5ua1ubL+7tSEAlT6vDEhVUeFAlxl1fn4WbktiamGJ1OA5nx6HTJKdl0b2pTAt1RfYmgnn59/2A6VrrpYCXnecUQgghhAs6k5XLvZ/HMGdDAg93b8R/h7fF20PaQwhrPdS9IUGVvXhpaRxaSzuJgqLjk1EKukVIdX5XZG8imKSU+hAYDixTSnmXwjmFEEII4WKOpGYy9IO1rNlznNdub8UTvSNQStpDCOv5+3jy+M1NWP/XSX7cedTqcBzKivhk2oQFElLF2+pQRBmwN2kbhqn+2VtrnQIEAZPsjkoIIYSowBbFJnH9a9HUf3op178WzaLYK7XbdXxxh08z+P01HDxxlhljOzCiYx2rQxLiIiM7hNOoehVeXRZHdq7N6nAcwvEzWWxNTKGHTAt1WXYlglrrdCAZuCF/Uy7S808IIYQosUWxSUxeuI2klAw0kJSSweSF25w2Gfxt9zGGfrAWrWH+A124qYlMMROOx8PdjWdubcb+E+l8ue6A1eE4hF92HUNrJBF0YXYlgkqpfwJPAZPzN3kCM+0NSgghhKiopiyPJyMn76JtGTl5/GvJDvYdO4PN5jxrmOZtSGDcZxsIq+rLNxO70Ly2v9UhCVGkbhHV6No4hHdX/ElKerbV4VguOv4oNfy9aSF/ty6rRH0ECxgMtAM2AWitD51rJyGEEEKI4tmTfIaklMxCnzuVnkOPt37Fz8eDNmGBtA4LoHVYIG3CA6jp7+NQ6+201vznp91Mjd5D18YhvH9nJH4+UhlUODalFM/0a8at76zi3RV7eH5Ac6tDskx2ro1Vu4/Tr3Uth3pvEaXL3kQwW2utlVIaQClVuRRiEkIIISqUM1m5TF3xJ5+s/gsFFDbmV93Pm3/c0oQtialsTUxh+m/7yM0fHazm502bsEDahAXQOtzcB1aypoh3dq6Np77eyjexSQyLCuPlwa2k/5hwGk1r+jMsKpwv1u5ndOe61A+pmB9tY/afJC0rV6aFujh7E8F5+VVDA5VS44FxwEf2hyWEEEK4Pq01i7cc4uWlcSSnZTE8KpyWYf68svTi6aG+nu78363NGNQulOEdzLbMnDx2Hj7N1oQUtiamsiUxhZ/jLlQ8rBtcyYwY5o8ctgz1p5KXvf/tX1lqeg4TZsawbt9J/nFzEx7u0UhGE4TT+fstTVi85RCvfR/Hh6OjrA7HEtHxyXi5u3F9oxCrQxFlyK7/EbTWbyqlbgZOAxHA81rrn0olMiGEEMKFxR0+zT8X72D9XydpHRbAh6Pb065OVQD8vD2ZsnwXh1IyqB3oy6TeEQxqF3rR8T6e7kTWqUpk/jEApzNz2J6Yen7UcOP+kyzZcggANwVNavhdmFIaFkhETT+8PEpntC7xVDpjP93AgRNneXt4Gwa3CyuV8wpR3qr7+fBQt4a8+eNu1u078f/s3Xl4VOXZx/HvnQUS1iggsgoqsogiGgHBBZcWrIq+rVtFraJ1e63axVbbt26tLS1VW+u+W5e6VxG1WgW0KChBRERAZV9U9j2BLPf7xzkJQ5hAkpnkTDK/z3XlysyZ5zxzz5knMPc8G4P2bRN1SPVu/NwVDNqvDc2b1u2XRxItS9bGmWbWFljtEe3EmZ+f7wUFBVE8tYiISLWtLyzmjv98wRNTFtEqJ4tfDu/FWfldyMiom56zlRuDJeDLk8MZS9axdksxAE2yMujToVVFr2G/Lq3Zt22LGscyc+l6Rj0+laLiUu4/7zAG76deBGnYiopLOe4vE9mzRRPG/u+Rdfb3mYoWrtrM0L9M5KZT+nDBkO5RhyM1ZGbT3L1aXdm1SvPNbBAwGlgD/A54AmgLZJjZ+e7+79rUKyIi0liVlTkvTFvKn/49h7VbtjFy4D78/LsH1PlcvnYtm3J87/Yc37s9EAxHXbq2kBlLwyGlS9bx/LSlPD45WDK/ZdMs+nZqzcFdWlcsStMpL3eHIZ4vT19W0WO5Z/MmbCgsZq9WOTx98UB6tNeacdLw5WRncu3wnvz02Rm8/Mkyvn9o+vRwj5+zAoDjerWPOBKpa7Xt770L+DXQGhgPnOjuU8ysF/BPQImgiIhI6NOl6/jtK7OYsWQdh+2zB4+PGEDfTq0jicXM6LJnM7rs2YyTD+4IQGmZM2/lJmbEzDd8ZNICikuDQT5tWzTh4DAp3LK1hH9MWURRcbDp9urN2zCDS4/priRQGpVT+3Xi0fcX8ud/z+XEvh3IbZIZdUj1YvycFey/Vwu6tmkWdShSx2qbCGa5+1sAZnaLu08BcPc5mhQuIiISWLN5G2PenMMzU5fQpnlTbj+zH//Tv1PKLaCSmWEc0L4lB7RvyRn5XQDYWlLKnK83VgwrnbFkHRPmriDeBBB3uP/dBZx/hIaRSeORkWH830l9OPP+yTz43/lcdXyPqEOqc5u2lvDhgtVcqCGhaaG2iWBZzO3CSo81nJ1uRURE6kBpmfP0h4v4y1tfsGlrCRcN6c7VJ/RoUHvpNc3KpF+XPPp1yeO88NimrSUcdOObcf+jX76u8scBkYZvQPc9GX7g3tz37jzOPrwLe7XKiTqkOjXpy1UUl7q2jUgTtU0E+5nZBsCA3PA24f3G/RciIiKyCwUL13DDK7P4/OsNDN6vDTeNOJADGsmQyRZNs+iYl8uyOElfx7zcCCISqXvXndiLd+Z8y21vfcGfTj846nDq1Pg539IyJ4vD9tlj94WlwavVmtHununurdy9pbtnhbfL7zecrztFRESSZMXGIn723Cecft9k1m7Zxt3nHMpTFw9sNElguWuH9SQ3e8e5UrnZmVw7rGdEEYnUrW5tm/OjI7rx3LQlfL58w+5PaKDKypwJc1dy9AHtyM5MzrYyktq0OYiIiEgCikvLePyDhfz17S/ZVlLG/x67H/977P51vnl7VMr3M9zdPocijclPjuvBCx8v5dbXP+fJiwam3DzfZPhs+XpWbtzK8RoWmjYa5/9SIiIi9eCDr1Zx49hZfLliE0N7tuPGUw6ke9vmUYdV507r30mJn6SV1s2yufr4Htz86udMmLuiUW6tMH7OCszgmAPaRR2K1BMlgiIiIjW0fF0ht74+m9c+/Zoue+by0Pn5HN97r0bZSyAigXMH7cMTkxdx62uzOapH4xs+OWHOCvp3yaNNi6ZRhyL1pHG1YBERkTq0taSUuyd8xfG3vcvbn3/Lz75zAP/56TGc0Ke9kkCRRi47M4PrTuzFvJWbeeajxVGHk1QrNhYxY+l6rRaaZtQjKCIiUg0T5q7g5rGzWLh6C8MObM//ndSHLntqw2WRdPKdPu0ZtO+e3PH2l4w4pBOtcxvHGokT564E4FglgmlFPYIiIiK7sHj1Fi5+vIALH51KhhmPjxrA/eflKwkUSUNmwSbza7ds454JX0UdTtKMn72CvVvl0KdDq6hDkXqkHkEREZE4CreVcu+787jv3XlkZRjXndiLUUO60yRL36GKpLO+nVrz/f6defT9hZw7aJ8G/6XQtpIyJn21ilP6ddQQ9zSj/81ERERiuDv//uwbTrj9Xe5850uGH7g3438+lMuO2U9JoIgAwX6aGRkw+t9zog4lYVMXrmHT1hJtG5GG1CMoIiISmrdyEzeNncV/v1xFz/YteeaSQQzat03UYYlIitm7dQ6XHr0ff3vnS0YNWcNh++wZdUi19s7sFTTJymDw/vq3Lt0oERQRkbTz8vRlO2yIftVx+zN/9WYembSAnKxMbjylD+cN2oesRrY8vIgkz6XH7Ms/P1rM78bN5l9XDG6wwyonzF3BEfu2oVkTpQXpRu+4iIiklZenL+P6l2ZSWFwKwLJ1hfzqpZkAnHFYZ345vBftWmofLRHZtWZNsvjFsJ788oVPefXTrxnRr2PUIdXY/JWbWLBqMxcO6RZ1KBIBfdUpIiJpZcybcyuSwFjtWjRlzBn9lASKSLX94NDO9OnQij+9MYeiOP+upLrxc1YAcGxPzQ9MR0oERUQkrSxfVxj3+KpNW+s5EhFp6DIzjP87qTfL1hXy6PsLow6nxibMXcEB7Vs0+JVPpXYiSQTNbLiZzTWzr8zsujiP/8zMPjezT83sHTPbJ4o4RUSk8dmzeZO4xzvm5dZzJCLSGAzevy0n9N6Luyd81aC+UNpYVMyH89doE/k0Vu+JoJllAncDJwJ9gB+aWZ9KxaYD+e5+MPAC8Of6jVJERBqjCXNWsG7LNiov6ZCbncm1w3pGEpOINHzXf683RcWl3PGfL6IOpdomfbmKkjLnOA0LTVtR9AgOAL5y9/nuvg14Bjg1toC7T3D3LeHdKUDneo5RREQamTdnfcMlTxTQu2Mrfn9aXzrl5WJAp7xc/vj9gzitf6eoQxSRBmq/di04d9A+/POjxXzx7caow9mll6cvY8jo8Vz+1McYsGztlt2eI41TFKuGdgKWxNxfCgzcRfmLgDfiPWBmlwCXAHTt2jVZ8YmISCPz6ozlXPPsJxzUqTWPjxpA69xsRg7SrAMRSZ6rju/Bix8v5Q+vz+axCwdEHU5clVdNduA3L88iIyNDX4aloZReLMbMzgXygTHxHnf3B9w9393z27VrV7/BiYhIg/DSx0u5+pnpHNZ1D568eCCtc7OjDklEGqE9mzfhquN6MHHuSt77YmXU4VQoK3M2FBWzdO0W/vD67J1WTS4sLmXMm3Mjik6iFEWP4DKgS8z9zuGxHZjZCcBvgGPcveHMvBURkZTx7NTFXPfSTI7Ytw0P/ShfGyaLSJ06f/A+3PvuV4x6bCqlZU7HvFyuHdaz1r1t7s7mbaVsLCpmY1EJGwrD3+H97be3399YVMyGwpKKY5u2leC+6+epajVladyi+B9xKtDDzLoTJIBnA+fEFjCz/sD9wHB3X1H/IYqISEP3xOSF/PaVWRxzQDvuP+8wcrIzow5JRBq5N2Z+w6aiEkrKgsxr2bpCrnvxU77dUMjh3dvETdQqkryiYjbEJHPlv8t2k8RlZRitcrNpmZMV/DTNplvbZrTMKT+WTaucLFrlZDP6jdms2VK8Ux1aNTk91Xsi6O4lZnYl8CaQCTzi7rPM7BagwN3HEgwFbQE8b2YAi919RH3HKiIiDdND/53P71+bHSzpPvJQmmYpCRSRujfmzblsK90xcysqKeOPb8QfeplhVCRsrcLfnfJyaZXTMjiWuz2Zi03qYn/nZGcQfl7erSZZGTvMEQStmpzOIhkj4+6vA69XOnZDzO0T6j0oERFpFO6e8BVj3pzL9w7am7+e1Z8mWSk9HV5EGpFdDbF89MLDY5K4ILFr1iSz2klcMpQPUR3z5lyWrytMeOiqNGyaLCEiIo2Cu3PH219y5ztfcuohHbntjH5kZSoJFJH60zEvl2VxksFOebkcmyL79Z3Wv5MSPwFSfNVQERGR6nB3/vTvudz5zpeccVhnbj/zECWBIlLvrh3Wk9xK85E19FJSlXoERUSkQXN3bhn3OY++v5CRA7vyu1P7kpFRf0OtRETKaeilNCRKBEVEpMEqK3N++8pnPPXhYi4c0o0bTu5Tr/NtREQq09BLaSiUCIqISINUWuZc9+KnPD9tKZcdsx+/Gt5TSaCIiEg1KREUEZEGp6S0jF88P4OXP1nO1cf34JoTeigJFBERqQElgiIi0qAUl5Zx9TPTeX3mN1w7rCf/e+z+UYckIiLS4CgRFBGRBmNrSSn/+9R03p79Lf93Um8uPmrfqEMSERFpkJQIiohIg1BUXMqlT0zj3S9W8rtTD+S8I7pFHZKIiEiDpURQRERS3pZtJVz8eAGT569m9PcP4uwBXaMOSUREpEFTIigiIiltY1Exox6byrRFa7ntjH58/9DOUYckIiLS4CkRFBGRlLW+sJgfPfIRM5et584f9ufkgztGHZKIiEijoERQRERS0trN2zjvkQ+Z+81G7hl5KMMO3DvqkERERBoNJYIiIpJyVm3ayrkPfcj8VZt54Lx8ju21V9QhiYiINCpKBEVEJKWs2FDEOQ99yNK1W3jkR4dzZI+2UYckIiLS6CgRFBGRlLF8XSHnPDiFlRu38viFAxi4b5uoQxIREWmUlAiKiEhKWLJmCz98cArrtxTzj4sGctg+e0QdkoiISKOlRFBERCK3YNVmRj44hc3bSnnqxwM5uHNe1CGJiIg0akoERUQkUl+t2Mg5D35ISZnzzx8Pok/HVlGHJCIi0ugpERQRkcjM/noD5z70IRkZxrOXDKJH+5ZRhyQiIpIWMqIOQERE0tNny9bzwwenkJ2ZoSRQRESknqlHUERE6t30xWs5/5GPaJWTzT9/PIiubZpFHZKIiEhaUY+giIjUq48WrOHchz5kz+ZNePZSJYEiIiJRUI+giIjUmw++WsVFjxfQIS+Hpy8exN6tc6IOSUREJC2pR1BEROrFu1+s5MLHptJ1z2Y8e8kRSgJFREQipB5BERGpc29//i1XPPUx++/VgicvHsiezZtEHZKIiEhaUyJYR16evowxb85l+bpCOublcu2wnpzWv1PUYe0g1WNUfIlRfIlRfImJjW+PZtms3VLMwZ1b849RA2ndLDvq8ERERNKeEsE68PL0ZVz/0kwKi0sBWLaukOtfmgmQMh/UUj1GxZcYxZcYxZeYyvGt2VJMhsEPB3RREigiIpIilAjWgTFvzq34AFSusLiUG8d+xurN2yKKakd3vvNFSseo+BKj+BKj+BITL74yh7+Pn8fZA/aJKCoRERGJZe4edQxJkZ+f7wUFBVGHAUD3616jcVxVEZHkMWDB6JOiDkNERKTRMrNp7p5fnbLqEawDHfNyWbaucKfjHVrn8O9rjo4gop0N/+t7fL2+aKfjqRKj4kuM4kuM4ktMVfF1zMuNIBoRERGJJ5JE0MyGA38DMoGH3H10pcePBv4KHAyc7e4v1H+UtXftsJ47zI8ByM3O5FfDe9E6NzXmx/xqeK+UjlHxJUbxJUbxJaaq+K4d1jPCqERERCRWvSeCZpYJ3A18B1gKTDWzse7+eUyxxcAFwC/qO75kKF+sIZVX9Ev1GBVfYhRfYhRfYlI9PhEREYlgjqCZHQHc5O7DwvvXA7j7H+OUfQwYV50ewVSaIygiIiIiIlLfajJHMKOug4mjE7Ak5v7S8FiNmdklZlZgZgUrV65MSnAiIiIiIiKNXRSJYNK4+wPunu/u+e3atYs6HBERERERkQYhikRwGdAl5n7n8JiIiIiIiIjUgygSwalADzPrbmZNgLOBsRHEISIiIiIikpbqPRF09xLgSuBNYDbwnLvPMrNbzGwEgJkdbmZLgTOA+81sVn3HKSIiIiIi0lhFso+gu78OvF7p2A0xt6cSDBkVERERERGRJKv37SPqipmtBBbtplhrYH01q6xu2d2VawusquZzNmQ1ubYNNYZk1Z9oPTU9P4p2D+nR9tXu66+uumz31S2vdh9IhXYPdRtHQ233NT1H7b760qHdJ7P+ht7uq1MuVdv9Pu5evVU03T1tfoAHkl12d+WAgqhfd6pd24YaQ7LqT7Semp4fRbsPyzT6tq92X3911WW7r255tfvkt4lUjaOhtvuanqN2H02bSOU4UuGzTiq0++qUawztvkFvH1ELr9ZB2ZrU2ZilwnWo6xiSVX+i9dT0fLX7upMK16GhtPtE66rLdl/d8qnwfqeCVLkOdRlHQ233NT1H7b76UuU6NJR/8xt6u69tHA1KoxkamqrMrMDd86OOQ6S+qe1LOlK7l3Skdi/pqDG0+3TrEYzCA1EHIBIRtX1JR2r3ko7U7iUdNfh2rx5BERERERGRNKMeQRERERERkTSjRFBERERERCTNKBEUERERERFJM0oERURERERE0owSwYiZWXMzKzCzk6OORaQ+mFlvM7vPzF4ws8ujjkekvpjZaWb2oJk9a2bfjToekfpgZvua2cNm9kLUsYjUpfAz/ePhv/Mjo46nOpQI1pKZPWJmK8zss0rHh5vZXDP7ysyuq0ZVvwKeq5soRZIrGe3e3We7+2XAmcCQuoxXJFmS1PZfdvcfA5cBZ9VlvCLJkKR2P9/dL6rbSEXqRg3/Br4PvBD+Oz+i3oOtBW0fUUtmdjSwCfiHu/cNj2UCXwDfAZYCU4EfApnAHytVMQroB7QBcoBV7j6ufqIXqZ1ktHt3X2FmI4DLgSfc/en6il+ktpLV9sPzbgOecveP6yl8kVpJcrt/wd1Pr6/YRZKhhn8DpwJvuPsnZva0u58TUdjVlhV1AA2Vu79nZt0qHR4AfOXu8wHM7BngVHf/I7DT0E8zGwo0B/oAhWb2uruX1WXcIolIRrsP6xkLjDWz1wAlgpLykvRvvgGjCT4oKAmUlJesf/NFGqqa/A0QJIWdgU9oIKMulQgmVydgScz9pcDAqgq7+28AzOwCgh5BJYHSENWo3YdfgHwfaAq8XqeRidStGrV94CfACUBrM9vf3e+ry+BE6khN/81vA9wK9Dez68OEUaQhq+pv4E7gLjM7CXg1isBqSolgCnD3x6KOQaS+uPtEYGLEYYjUO3e/k+CDgkjacPfVBPNiRRo1d98MXBh1HDXRILotG5BlQJeY+53DYyKNmdq9pCu1fUlHaveS7hrN34ASweSaCvQws+5m1gQ4GxgbcUwidU3tXtKV2r6kI7V7SXeN5m9AiWAtmdk/gclATzNbamYXuXsJcCXwJjAbeM7dZ0UZp0gyqd1LulLbl3Skdi/prrH/DWj7CBERERERkTSjHkEREREREZE0o0RQREREREQkzSgRFBERERERSTNKBEVERERERNKMEkEREREREZE0o0RQREREREQkzSgRFBGRpDGzO8zsmpj7b5rZQzH3bzOzn+2mjg+q8TwLzaxtnONDzWxwFeeMMLPrdlNvRzN7Ibx9iJl9r4bnX2Bmd4W3LzOz83f3Wnb3GmpbT10IYxsXdRwiIpK4rKgDEBGRRuV94Ezgr2aWAbQFWsU8Phj46a4qcPe4iVw1DQU2ATslk+4+Fhi7m+deDpwe3j0EyAder+75leq6r7plKxlKzGtIoB4REZEqqUdQRESS6QPgiPD2gcBnwEYz28PMmgK9gY8BzOxaM5tqZp+a2c3lFZjZpvB3hpndY2ZzzOw/Zva6mZ0e81w/MbOPzWymmfUys27AZcBPzewTMzsqNrBKvXWPmdmdZvaBmc0vr9fMupnZZ2bWBLgFOCus66xK559iZh+a2XQze9vM2le+EGZ2k5n9Iuxl/CTmp9TM9olXR7zXUF5PWOchZjYlvGb/MrM9wuMTzexPZvaRmX1R+bWHZTqY2XthvZ+VlzGz4eF1nGFm74THBpjZ5DC2D8ysZ5z6mpvZI+FzTjezU6tsFSIiknKUCIqISNKEPWolZtaVoPdvMvAhQXKYD8x0921m9l2gBzCAoOftMDM7ulJ13we6AX2A89ieYJZb5e6HAvcCv3D3hcB9wB3ufoi7/3c34XYAjgROBkZXeh3bgBuAZ8O6nq107iRgkLv3B54BflnVk7j78rCOQ4AHgRfdfVG8OqrxGv4B/MrdDwZmAjfGPJbl7gOAayodL3cO8GYYRz/gEzNrF8b0A3fvB5wRlp0DHBXGdgPwhzj1/QYYHz7nscAYM2te1XUQEZHUoqGhIiKSbB8QJIGDgduBTuHt9QRDRwG+G/5MD++3IEgM34up50jgeXcvA74xswmVnuel8Pc0gqSxpl4O6/48Xo/ebnQGnjWzDkATYMHuTjCzIcCPCV5Xjesws9ZAnru/Gx56HHg+pkjs9egWp4qpwCNmlk3w2j8xs6HAe+6+AMDd14RlWwOPm1kPwIHsOPV9FxhR3lsJ5ABdgdm7eh0iIpIa1CMoIiLJ9j5B4ncQwdDQKQS9eYPZPnfPgD+W95S5+/7u/nANn2dr+LuU2n2xuTXmttXw3L8Dd7n7QcClBElQlcJk72HgTHffVJs6qmGX18Pd3wOOBpYBj+1mAZrfARPcvS9wShWxGUFPYvl72NXdlQSKiDQQSgRFRCTZPiAYbrnG3UvDXqY8gmSwPBF8ExhlZi0AzKyTme1VqZ73gR+EcwXbEyyisjsbgZZJeA27q6s1QUIF8KNdVRL2wD1PMKTzi2rUEfd53X09sDZm/t95wLuVy+0ijn2Ab939QeAh4FCCJP1oM+seltkzTmwXVFHlmwTzNC08t391YxERkegpERQRqSUz62pmm8wsMwl1PWZmv09GXFXUX+1Yk/C6ZhKsFjql0rH1wF/M7Pfu/hbwNDDZzGYCL7Bz8vMisBT4HHiksl8WAAAgAElEQVSSYJGZ9bt57leB/4m3WEwtTAD6lC8WU+mxm4DnzWwasGo39QwmSILvjFkwpuMu6jiTIEmO9xp+RDAX71OCuZW3xHm+a4A2cY4PBWaY2XTgLOBvBENVZwEvmdkMoHwu5J+BP5rZQqCqnsPfEQwZ/dTMZhEMc30Skvu3EStcPGduMuusK+ECPhdHHYeISFXM3aOOQUQkpYUfhtsTDLkrd0C4MEqynuMxYKm7/1+cxy4ALnb3Iys/1tDs6nVWUb6Fu28yszbAR8AQd/+mLmOMmplNBJ5094fiPNaNYC5htruXJPl5E6rbzG4C9nf3c5MYkwM93P2rZNVZX3b1PoqIpAItFiMiUj2nuPvbUQdRFTPLdPfS3ZdscMaZWR7BYiq/a+xJoIiISH3R0FARkVqyYM85N7Os8P5EM/udmb1vZhvN7C0zaxtT/nkz+8bM1of7uR1YjefoTbCdwBHhULt14fHHzOxeC/bW2wwca2Ynhfu5bTCzJWEPTY1jrcXrOt/MFpnZajP7rZktNLMTqnkNf2xmX5nZGjMbGw6ZxAJ3EGwdsS9Bb2xB+Nj3zOzzMJZltn3Vyth6m5rZOjPrG3OsnZkVmtleZtbWzMaFZdaY2X/NbKf/E83sZjP7e3g728w2m9mY8H6umRVZOK/OzAZZsOfeOgv25BsaU0/FMEEzyzSz28xslZktMLMrY693aJ8qrnf5qqrrwvZQeUuN8v0Ly4dolr+XPzKzxeFz/iZe2Xh1W7B34qSY8n8L29YGM5tmVQy/jW1DYT2bYn6KLOhlj92vcJ2ZfW1md1mwhyNmVh7PjPC8s8xsqJktjXme3uG1XWdms8xsRMxjj5nZ3Wb2WngdPzSz/aqIN8fMngzb8DoL9rdsHz62p5k9ambLzWytmb0cHt8jbEMrw+PjzKxzvPrD8qPMbHZY9k0L5myKiERGiaCISHKdA1wI7EXQixWbpLxBsEXCXgTz3Z7aXWXhKoyXAZPdvYW751V6rlsJ5tZNAjYTzOfKA04CLjez02oZa7XKmlkf4B5gJMG+fK0JtovYLTM7DvgjwZy4DsAigv30INia4GjggLDOM4HV4WMPA5e6e0ugLzC+ct3uvpVgO4Ufxhw+E3jX3VcAPyeYf9iOYNjvrwm2SajsXbYvUnM48E0YFwTz/ua6+xoz6wS8Bvwe2JPg+rxowT59lf0YOJFgjt+hQLz3qKr3pvy588L2MDnOufEcCfQEjgdusOALhsqqU/fUMO49CeZ4Pm9mu1zt1N3L224LYA+CfSX/GT5cCvyUYE7pEWF8V4TnlcfTLzx/h70cLViE51XgLYLr9BPgKTPrGVPsbODm8Hm/Ivh7iedHBO2sC8H8ysuAwvCxJ4BmwIHh89wRHs8AHgX2Idg2oxC4K17lZnYqQRv7PkGb+2/MNRARiYQSQRGR6nk57ClYV94jUIVH3f0Ldy8EniP40AyAuz/i7hvDJOUmoJ8Fe8PV1ivu/r67l7l7kbtPdPeZ4f1PCT5oHlObWGtQ9nTgVXefFLMJe3Unn48EHnH3j8Nrcj1Bz2c3oJggwe1FMJ99trt/HZ5XTLCISyt3X+vuH1dR/9MEiUC5c8Jj5XV0APZx92J3/6/HnzQ/GehhwRzFowmS0E4WrHZ6DNtX7TwXeN3dXw+v/38IejC/F6fOM4G/uftSd19Lpc3sQzV5b6rjZncvdPcZwAyCDeVrzN2fdPfV7l7i7rcBTQkSzOq6k2BV1N+E9U1z9ylhfQuB+9l1m401iGD/ydHuvs3dxwPj2DH5/5e7fxTOeXyKqq9jMUECuH+40u00d99gwbYfJwKXhW2tuHwfx/A6vOjuW9x9I0GSWVXslxFslzI7jOUPwCHqFRSRKCkRFBGpntPcPS/82VUvW+wcti0EH1TLhwOONrN5ZrYBWBiWaUvtLYm9Y2YDzWxCOFRtPcGHz13VHzfWGpbtGBuHu29he8/d7nQk6AUsP3dTeG6n8EP9XcDdwAoze8DMWoVFf0CQYC0ys3fjDY8MTQCahdelG0ES8K/wsTEEPURvmdl8M7suXgVhIlZA8AH/aILE7wNgCDsmgvsAZ8R8WbCOoBeuQxWvO/a9WxKnTE3em+pISn1m9otweOP68DW2pppt2MwuJehdPcfdy8JjB4RDKr8J/y7+UN36CK9jeV2hRezYI13d1/0EwXYYz4RDQP8c9jh2IdgGZW2c19PMzO63YFj0BoKhtXkWf6XUfYC/xbSNNQT7MFar91xEpC4oERQRqR/nAKcCJxB8eO4WHq/ORuZV9bBVPv40MBbo4u6tCeYW1nSj9Jr6GqiYF2VmucTfuiCe5QQfkMvPbR6euwzA3e9098MI5gkeAFwbHp/q7qcSDNN7maDHbCfh4jnPEfQQ/RAYF/bcEPbM/tzd9wVGAD8zs+OriPNd4DigP8HQyHeBYcAAts+rWwI8EfNlQZ67N3f3eL19O1wzgmSjuupyqe9d1h3OB/wlQY/mHuEw5fVUo42F5/4OONXdN8Q8dC8wh2Bl0FYEwyer22aXA11sx7mdXdm+/2G1hT19N7t7H4LtPk4mGGa9BNjTggWLKvs5QW/owDD28qGs8eJfQjCcObZ95Lr7B3HKiojUCyWCIiL1oyWwlaDHqxlBz0d1fQt0Ll9EYzfPscbdi8xsAEHyWddeAE4xs8FhfDdR/Q/y/wQuNLNDzKwpwTX50N0XmtnhYU9eNsHcxyKgzMyamNlIM2vt7sXABqCs6qfgaYI980ayfVgoZnayme1vZkaQzJTuop53CZKCz8PhrxOBi4EF7r4yLPNkeB2Ghb2/OeHCJvEWD3kOuNrMOoUJxq92e6W2WxnGuW8NzklW3S2BkrBclpndALSqomwFM+tC8JrPd/cv4tS5AdhkZr2Ayys9/u0u4vmQoJfvlxYs5DMUOIXt80yrzcyONbODwt68DQRDRcvC4chvAPeEi8Nkm1l5wteSYF7gOgsWDLpxF09xH3C9hQtEmVlrMzujpnGKiCSTEkERkfrxD4Jha8sINkifsuviOxhPsOn3N2a2q83LrwBuMbONBHP14vaUJZO7zyJYpOMZgp6uTcAKgqR3d+e+DfyWYOP4r4H92D6nrxXwILCW4LqtJhjOCXAesDAcjncZQZJX1XN8SJBIdiT4QF+uB/B2GO9k4B53n1BFNR8AuWzv/fucIDEtv4+7LyHo8f01QaK0hKAHM97/sw8SLHDyKTAdeJ0gwdrt9h/h0NtbgffDYYaDdndOdVWj7jeBfwNfELwnRcQf1lrZ8QQL8rxg21cOnRU+9guCLyw2ElyXZyudexPweBjPmZXi3UaQ+J0IrCJYtOh8d59Tnddbyd4EX2psAGYTJP9PhI+dR5AYziFo29eEx/9K0C5WEfw9/7uqyt39X8CfCIaebgA+C+MWEYmMNpQXEZGkCRdRWUcw1G9B1PE0BGZ2InCfu2vhEBERqTfqERQRkYSY2SnhwhnNgb8AM9m+GI5UYsH+g9+zYI+9TgRDCv+1u/NERESSqd4TwXDexEcWbLY7y8xujlPmgnDVu0/Cn4vrO04REam2UwkW7lhOMOTy7Cq2YpCAEextt5ZgaOhsgqG8IiIi9abeh4aGE/Obu/umcBGAScDV7j4lpswFQL67X1mvwYmIiIiIiKSBrPp+wvBb4k3h3ezwR98ci4iIiIiI1JN6TwQh2FgZmAbsD9wdrupW2Q/CJZq/AH4arshWuZ5LgEsAmjdvflivXr3qMGoRERERkeqZuWw9e7VsSvtWOVGHImlk2rRpq9y9XXXKRrpqaLh/0r+An7j7ZzHH2wCb3H2rmV0KnOXux+2qrvz8fC8oKKjbgEVEREREdqO0zNnv16/zs+8cwFXH94g6HEkjZjbN3fOrUzbSVUPdfR0wARhe6fhqdy/fg+oh4LD6jk1EREREpDbKwo6WDIs4EJFdiGLV0HZhTyBmlgt8h2CT1tgyHWLujiBYUU1EREREJOWVJ4LBGokiqSmKOYIdgMfDeYIZwHPuPs7MbgEK3H0scJWZjQBKgDXABRHEKSIiIiJSY+UzrzKUCEoKi2LV0E+B/nGO3xBz+3rg+vqMS0REREQkGdJ9aGhxcTFLly6lqKgo6lAarZycHDp37kx2dnat64hk1VARERERkcaqLOwRTNcOwaVLl9KyZUu6deum4bF1wN1ZvXo1S5cupXv37rWuJ9LFYkREREREGpvtPYLpmQQVFRXRpk0bJYF1xMxo06ZNwj2uSgRFRERERJLIK3oE0zcRSufXXh+ScX2VCIqIiIiIJJGn+RxBaRiUCIqIiIiIJFGZVg2N3MKFC+nbt2+d1D1x4kROPvlkAMaOHcvo0aPr5HnqmhaLERERERFJonRfNbSmXp6+jDFvzmX5ukI65uVy7bCenNa/U9RhVcuIESMYMWJE1GHUinoERURERESSSBvKV9/L05dx/UszWbauEAeWrSvk+pdm8vL0ZQnXXVJSwsiRI+nduzenn346W7Zs4ZZbbuHwww+nb9++XHLJJRXDeO+880769OnDwQcfzNlnnw3A5s2bGTVqFAMGDKB///688sorOz3HY489xpVXXgnABRdcwFVXXcXgwYPZd999eeGFFyrKjRkzhsMPP5yDDz6YG2+8MeHXlgzqERQRERERSSJP8+0jYt386iw+X76hysenL17HttKyHY4VFpfyyxc+5Z8fLY57Tp+OrbjxlAN3+9xz587l4YcfZsiQIYwaNYp77rmHK6+8khtuCLYvP++88xg3bhynnHIKo0ePZsGCBTRt2pR169YBcOutt3LcccfxyCOPsG7dOgYMGMAJJ5ywy+f8+uuvmTRpEnPmzGHEiBGcfvrpvPXWW3z55Zd89NFHuDsjRozgvffe4+ijj97ta6hL6hEUEREREUmidN8+oiYqJ4G7O14TXbp0YciQIQCce+65TJo0iQkTJjBw4EAOOuggxo8fz6xZswA4+OCDGTlyJE8++SRZWUFf2VtvvcXo0aM55JBDGDp0KEVFRSxeHD85LXfaaaeRkZFBnz59+Pbbbyvqeeutt+jfvz+HHnooc+bM4csvv0z49SVKPYIiIiIiIknkFYvFRBtHKthdz92Q0eNZtq5wp+Od8nJ59tIjEnruykNzzYwrrriCgoICunTpwk033VSxF99rr73Ge++9x6uvvsqtt97KzJkzcXdefPFFevbsuUM95QlePE2bNq24XT7s1N25/vrrufTSSxN6PcmmHkERERERkSTSHMHqu3ZYT3KzM3c4lpudybXDelZxRvUtXryYyZMnA/D0009z5JFHAtC2bVs2bdpUMYevrKyMJUuWcOyxx/KnP/2J9evXs2nTJoYNG8bf//73ioRu+vTptYpj2LBhPPLII2zatAmAZcuWsWLFikRfXsLUIygiIiIikkSu7SOqrXx10LpYNbRnz57cfffdjBo1ij59+nD55Zezdu1a+vbty957783hhx8OQGlpKeeeey7r16/H3bnqqqvIy8vjt7/9Lddccw0HH3wwZWVldO/enXHjxtU4ju9+97vMnj2bI44IejhbtGjBk08+yV577ZXwa0yElWe4DV1+fr4XFBREHYaIiIiIpLlFqzdzzJiJ3H5mP75/aOeow6l3s2fPpnfv3lGH0ejFu85mNs3d86tzvoaGioiIiIgkkTaUl4ZAiaCIiIiISBJtnyMYcSAiu6BEUEREREQkiVyLxUgDoERQRERERCSJtH2ENARKBEVEREREkkhzBKUhUCIoIiIiIpJE5XME1SMoqUyJoIiIiIhIEmlD+egtXLiQvn37Vrv8Y489xvLly3db5sorr0w0tJShRFBEREREJIm0oXwNffoc3NEXbsoLfn/6XL2HUJ1EsK6UlJRE8rxZkTyriIiIiEgjVdEjGHEcDcKnz8GrV0FxYXB//ZLgPsDBZyZUdUlJCSNHjuTjjz/mwAMP5B//+Ad/+ctfePXVVyksLGTw4MHcf//9vPjiixQUFDBy5Ehyc3OZPHkyn332GVdffTWbN2+madOmvPPOOwAsX76c4cOHM2/ePP7nf/6HP//5zwC0aNGCq6++mnHjxpGbm8srr7xC+/btWbhwIaNGjWLVqlW0a9eORx99lK5du3LBBReQk5PD9OnTGTJkCK1atWLBggXMnz+fxYsXc8cddzBlyhTeeOMNOnXqxKuvvkp2dnZC16My9QiKiIiIiCRRxWIx+qQNb1wHj55U9c8rV25PAssVFwbHqzrnjeuq9dRz587liiuuYPbs2bRq1Yp77rmHK6+8kqlTp/LZZ59RWFjIuHHjOP3008nPz+epp57ik08+ITMzk7POOou//e1vzJgxg7fffpvc3FwAPvnkE5599llmzpzJs88+y5IlSwDYvHkzgwYNYsaMGRx99NE8+OCDAPzkJz/hRz/6EZ9++ikjR47kqquuqohv6dKlfPDBB9x+++0AzJs3j/HjxzN27FjOPfdcjj32WGbOnElubi6vvfZaou/ETtQ8RURERESSSPsI1kDp1podr4EuXbowZMgQAM4991wmTZrEhAkTGDhwIAcddBDjx49n1qxZO503d+5cOnTowOGHHw5Aq1atyMoKBlIef/zxtG7dmpycHPr06cOiRYsAaNKkCSeffDIAhx12GAsXLgRg8uTJnHPOOQCcd955TJo0qeJ5zjjjDDIzMyvun3jiiWRnZ3PQQQdRWlrK8OHDATjooIMq6ksmDQ0VEREREUkibR8R48TRu378jr7BcNDKWneBCxPrBauciJsZV1xxBQUFBXTp0oWbbrqJoqKiGtXZtGnTituZmZkV8/uys7Mrni/2+K40b948bt0ZGRk71JeRkVEn8wjVIygiIiIikkSu7SOq7/gbIDt3x2PZucHxBC1evJjJkycD8PTTT3PkkUcC0LZtWzZt2sQLL7xQUbZly5Zs3LgRgJ49e/L1118zdepUADZu3FjrRGzw4ME888wzADz11FMcddRRtX49yaYeQRERERGRJFKPYA2ULwjzzi2wfim07hwkgQkuFANBQnf33XczatQo+vTpw+WXX87atWvp27cve++9d8XQT4ALLriAyy67rGKxmGeffZaf/OQnFBYWkpuby9tvv12rGP7+979z4YUXMmbMmIrFYlKFlX9j0dDl5+d7QUFB1GGIiIiISJqbMn81Zz8whad/PJDB+7WNOpx6N3v2bHr37h11GI1evOtsZtPcPb8652toqIiIiIhIEm3fPkI9gpK6lAiKiIiIiCTR9g3lo41DZFeUCIqIiIiIJFFFIpjGmWBjmX6WqpJxfZUIioiIiIgkUVmarxqak5PD6tWrlQzWEXdn9erV5OTkJFSPVg0VEREREUmisjTfUL5z584sXbqUlStXRh1Ko5WTk0Pnzp0TqkOJoIiIiIhIEnmabx+RnZ1N9+7dow5DdkNDQ0VEREREkijdh4ZKw6BEUEREREQkico3lNf2EZLKlAiKiIiIiCTR9jmCEQcisgtKBEVEREREkijd5whKw6BEUEREREQkicq3TcjQJ21JYfXePM0sx8w+MrMZZjbLzG6OU6apmT1rZl+Z2Ydm1q2+4xQRERERqY0y9QhKAxDF9xRbgePcvR9wCDDczAZVKnMRsNbd9wfuAP5UzzGKiIiIiNSKVg2VhqDeE0EPbArvZoc/XqnYqcDj4e0XgOMtXXfkFBEREZEGJd03lJeGIZKRy2aWaWafACuA/7j7h5WKdAKWALh7CbAeaBOnnkvMrMDMClauXFnXYYuIiIiI7JZXbB8hkroiSQTdvdTdDwE6AwPMrG8t63nA3fPdPb9du3bJDVJEREREpBa2Dw1VKiipK9K1jNx9HTABGF7poWVAFwAzywJaA6vrNzoRERERkZrTYjHSEESxamg7M8sLb+cC3wHmVCo2FvhRePt0YLyXr8MrIiIiIpLCXBvKSwOQFcFzdgAeN7NMgkT0OXcfZ2a3AAXuPhZ4GHjCzL4C1gBnRxCniIiIiEiNVWwor2VDJYXVeyLo7p8C/eMcvyHmdhFwRn3GJSIiIiKSDNo+QhqCSOcIioiIiIg0NpojKA2BEkERERERkSSq2Ecw4jhEdkWJoIiIiIhIErk2lJcGQImgiIiIiEgSbR8aGm0cIruiRFBEREREJIlcG8pLA6BEUEREREQkibRYjDQESgRFRERERJKoYrEYfdKWFKbmKSIiIiKSRK4eQWkAlAiKiIiIiCSRto+QhkCJoIiIiIhIEmmOoDQESgRFRERERJLk5enLuHfiVwAcf9tEXp6+LOKIROLLijoAEREREZHG4OXpy7j+pZkUFpcCsHx9Ede/NBOA0/p3ijI0kZ2oR1BEREREJAnGvDm3IgksV1hcypg350YUkUjVlAiKiIiIiCTB8nWFNTouEiUlgiIiIiIiSdAxL7dGx0WipERQRERERCQJrh3Wk8yMHVcKzc3O5NphPSOKSKRqWixGRERERCQJjuzRFtxp1iSTwm2ldMzL5dphPbVQjKQkJYIiIiIiIknw2PsLKQPGXnkk++/VIupwRHZJQ0NFRERERBK0saiYxycvZFifvZUESoOgRFBEREREJEFPf7iYjUUlXD50v6hDEakWJYIiIiIiIgkoKi7loUkLGLJ/G/p1yYs6HJFqUSIoIiIiIpKAlz5exsqNW7li6P5RhyJSbUoERURERERqqaS0jPvfm0e/zq0ZvF+bqMMRqbakJIJmlmFmrZJRl4iIiIhIQ/H6Z9+waPUWLh+6H2a2+xNEUkStE0Eze9rMWplZc+Az4HMzuzZ5oYmIiIiIpC53596J89i3XXO+22fvqMMRqZFEegT7uPsG4DTgDaA7cF5SohIRERERSXETv1jJ7K83cNkx+5GRod5AaVgSSQSzzSybIBEc6+7FgCcnLBERERGR1HbvxHl0aJ3DaYd0ijoUkRpLJBG8H1gINAfeM7N9gA3JCEpEREREJJVNW7SGjxas4eKj9qVJltZflIYnq7YnuvudwJ0xhxaZ2bGJhyQiIiIiktrunTiPvGbZ/HBAl6hDEamVRBaLuTpcLMbM7GEz+xg4LomxiYiIiIiknLnfbOTt2Su4YHA3mjWpdb+KSKQS6cceFS4W811gD4KFYkYnJSoRERERkRR137vzaNYkkwsGd4s6FJFaSyQRLF8a6XvAE+4+K+aYiIiIiEijs2TNFsbOWM45A7qS16xJ1OGI1FoiieA0M3uLIBF808xaAmXJCUtEREREJPU88N58MgwuOqp71KGIJCSRQc0XAYcA8919i5m1AS5MTlgiIiIiIqll5catPFewhO/370yH1rlRhyOSkERWDS0zs87AOWYG8K67v5q0yEREREREUsij7y9gW2kZlxyzb9ShiCQskVVDRwNXA5+HP1eZ2R+SFZiIiIiISKrYUFTME5MXcWLfvdmvXYuowxFJWCJDQ78HHOLuZQBm9jgwHfh1MgITEREREUkVT01ZzMatJVx+zP5RhyKSFIksFgOQF3O7dYJ1iYiIiIiknKLiUh6etICjerTloM76yCuNQyI9gn8EppvZBIJtI44GrktKVCIiIiIiKeKFaUtZtWkrlx9zSNShiCRNrXsE3f2fwCDgJeBF4Ah3f3Z355lZFzObYGafm9ksM7s6TpmhZrbezD4Jf26obZwiIiIiIrVVUlrGA+/Np1+XPI7Yr03U4YgkTY17BM3s0EqHloa/O5pZR3f/eDdVlAA/d/ePw70Hp5nZf9z980rl/uvuJ9c0PhERERGRZHlt5tcsXrOF35zUm3ClfJFGoTZDQ2/bxWMOHLerk939a+Dr8PZGM5sNdCJYeVREREREJCW4O/dOnMf+e7XgO73bRx2OSFLVOBF092OT9eRm1g3oD3wY5+EjzGwGsBz4hbvPinP+JcAlAF27dk1WWCIiIiIiTJy7kjnfbOQvZ/QjI0O9gdK4JLpqaK2ZWQuCuYXXuPuGSg9/DOzj7v2AvwMvx6vD3R9w93x3z2/Xrl3dBiwiIiIiaeWeiV/RsXUOI/p1jDoUkaSLJBE0s2yCJPApd3+p8uPuvsHdN4W3XweyzaxtPYcpIiIiImlq6sI1TF24lh8fvS9NsiLrOxGpM/Xeqi2YZfswMNvdb6+izN5hOcxsAEGcq+svShERERFJZ/dOnMcezbI56/AuUYciUidqvY9gnNVDAdYDi9y9ZBenDgHOA2aa2SfhsV8DXQHc/T7gdOByMysBCoGz3d1rG6uIiIiISHXN/noD4+es4GffOYBmTRLZdlskdSXSsu8BDgU+JdhQvi8wC2htZpe7+1vxTnL3SWH5Krn7XcBdCcQmIiIiIlIr9707j+ZNMjn/iH2iDkWkziQyNHQ50D9crOUwgtU/5wPfAf6cjOBEREREROrT4tVbeHXGcs4Z2JW8Zk2iDkekziSSCB4Qu6VDuCF8L3efn3hYIiIiIiL174H/ziMzw7joyH2jDkWkTiUyNHSWmd0LPBPePwv43MyaAsUJRyYiIiIiUo9WbtzKcwVL+cGhndm7dU7U4YjUqUR6BC8AvgKuCX/mh8eKgaRtOi8iIiIiUh8eeX8BxaVlXHrMflGHIlLnat0j6O6FwG3hT2Wbah2RiIiIiEg921BUzJOTF/G9vh3o3rZ51OGI1LlEto8YAtwE7BNbj7trQLWIiIiINChPTlnExq0lXD5UvYGSHhKZI/gw8FNgGlCanHBEREREROpXUXEpj0xawFE92tK3U+uowxGpF4kkguvd/Y2kRSIiIiIiEoHnpy1l1aZtXDF0/6hDEak3iSSCE8xsDPASsLX8oLt/nHBUIiIiIiL1oKS0jAfem8chXfIYtO+eUYcjUm8SSQQHhr/zY445cFwCdYqIiIiI1JvXZn7NkjWF/PakPphZ1OGI1JtEVg3VFhG78PL0ZYx5cy7L1xXSMS+Xa4f15LT+naIOawepHqPiS4ziS4ziS0yqxyciAuDu3DtxHj32asEJvdtHHY5IvVEE0TcAACAASURBVKpxImhm57r7k2b2s3iPu/vtiYfVsL08fRnXvzSTwuJgDZ1l6wq5/qWZACnzQSjVY1R8iVF8iVF8iUn1+EREyk2Yu4I532zktjP6kZGh3kBJL+buNTvB7FJ3v9/Mboz3uLvfnJTIaig/P98LCgqieOqdDBk9nmXrCnc63rxJJj84rDPx/pmpyVCEeEUtTq3xywWembqYTVt3Xuy1RdMszhnYdcfaLPam7VR3bNny47HxxC0bczDe+Q9PWsDGopKd4muZk8WFQ7oHd8K2W96C3cHDe+XNOt5jsb/K2/+uylf+E3F3Xpi2lM3bdr5+zZtmcsZhXXZ4LYbF3I45Ht6wmItQZdlKx4k5t/I1N4MH/zs/7vVrlZNVp5vkVrcZ3zdxHhuqeH8vOWrf8L0J3oPgd3Ag3vGK98q3v6exj8H28pQ/XkU9hPfHzljOljjvb7MmmZzYt0NFnRV2aj9e+aGd2lhsuR2aWEzMsefF3p74xQqKist2ii8nO4Pje7UHK28bVtFGDMgIb5S3p+2PhffDxhj7WEZFHdvb107nh2XK635yyiI2bd35/e2Ul8v712n2gIikjtPv/YCv1xcx8dqhZGdmRB2OSMLMbJq75+++ZC0SwVSVSolg9+teo6qrmtcse6djVb0F8d6buEXjHIxXLra+eElMuZzs7f8Q+s6fddnx86/vVDbeB93Y48lscjskR8R8IGXnBKn8w3Hlx+LVUXG/isfWFxZXGVOrnKwdrlVscrBzUuA7XrcqjscmDI3kT7ZWticftkMSQ2xSskOSEvOVROyxOPXEJkArNm7d+clDnfJyd4in8u1dfVmyQ9uq9OCOX4jsWG7Huoy5326sMr792jWvaEsViTFBuynzmiXEZbFlyttvnHorzg9vF5fGb6QGLBh9UpWxi4jUp48WrOHM+ydz0yl9uKD8S2aRBq4miWAiG8q3A34MdGPHDeVH1bbOxqJjXm7cHsFU+ja8ql7LKGOM7Z078s/jWb6uaKcynfJyeP+64+s7tJ2kyvWL16Pp7hw9ZkLc69exdQ4Tr9399N4deruqHUv1yx73l4ksXx8nvrwc3gvj2yHZq+fJ+6ny/lZlV/G98/Oh9R9QJVXF16F1TgTRiIjEd+/Er9izeRPOOrxr1KGIRCKRPvBXgNbA28BrMT9p79phPcnNztzhWG52JtcO6xlRRDtLxRiDnhojI8P45bBeVcTXK6LodpQq1y/2mmVkGJkZRlZmRpXX75fDe9EkK2O3P02zMmv8k5Nd/Z9fDq8ivmG9yMrMICszg8zwNUWxgluqvL9VaYjxAeRkZ7J6U9W9rSIi9eXz5RuYMHclFw7uRm6Tnf+9EkkHiWwf0czdf5W0SBqR8sUQUnnFvFSPUfElRvElRvElJl58x/Zqx/MFSxlx1/s8eH4+fTq2ijhKEUln9707j+ZNMjn/iG5RhyISmVrPETSz3wMfuPvryQ2pdlJpjqCIiOxsxpJ1XPJEARsKS7j9zH6ceFCHqEMSkTS0aPVmjv3LRC4+al9+/b3eUYcjklQ1mSOYyNDQq4FxZlZoZhvMbKOZbUigPhERacT6dcnj1SuPpFeHllz+1Mfc/p8vKCtL49WPRCQSD7w3n6yMDC46UgvESHqrdSLo7i3dPcPdc929VXhfY31ERKRKe7XK4ZlLBnH6YZ25850vufypaWyOs9WEiEhdWLGxiOenLeUHh3WifSstYCXprcaJoJn1Cn8fGu8n+SGKiEhj0jQrkzGnH8xvT+7Dfz7/lh/c+wFL1myJOiwRSQOPTFpISWkZlx5dd3vqijQUtVks5mfAJcBtcR5zIPq11UVEJKWZGRcd2Z0D2rfgyqenM+KuSdw98lAG79c26tBEpJFaX1jMk1MW8b2DOtCtbfOowxGJXI17BN39kvD3sXF+lASKiEi1HdWjHa/87xDatGjKeQ9/xD8mL6S2i5iJiOzKk1MWsWlrCZcdo95AEUhs+wjMrC/QB6gYZO3u/0g0KBERSR/d2jbnX1cM5ppnPuGGV2Yx++sN3DyiL02yElnPTERku6LiUh59fwHHHNCOvp1aRx2OSEqo9f+yZnYj8Pfw51jgz8CIJMUlIiJppGVONg+cn88VQ/fjnx8tYeRDU1ilzedFJEmeK1jCqk3buHyoegNFyiXydevpwPHAN+5+IdAP0FcsIiJSK5kZxi+H9+LOH/Zn5rL1nHrX+3y2bH3UYYlIA1dcWsb9787n0K55DOy+Z9ThiKSMRBLBQncvA0rMrBWwAuiSnLBERCRdjejXkRcuG0yZO6ff9wHjPl0edUgi8v/t3Xd4nNWZ/vHvo2oVS7Itucm9YIorGGNsAyYkkNADLLBAEgKEFNiEFHaT7CabkN3A/simsGwoCSShBgKEDg5LAOMG2GBsMO5yL7IsW1av5/fHGXlG0shWGWlmNPfnuuaamXfeeXUkvzOee55T4tiLq3ax82A1X58/ATOLdnNEYkZ3guByM8sDfgesAN4HlkakVSIiktAmF+by/M3zmDw8l5sf+4BfLFinxedFpNOamhz3vLmJY4Zkc9axg6PdHJGY0qUgaP7rlNudcwedc/cCnwG+FOgiKiIi0m0F/dN59CuncMXMkdz9xkZufHgF5TX10W6WiMSRv68tZv3eCr52xniSklQNFAnVpSDo/NzeL4fc3+KcWxWxVomIiOAXn7/j0in85ILjeWNdMZf8dglb91dGu1kiEgecc/z2zY0U5mVwwbTh0W6OSMzpTtfQ983s5Ii1REREJAwz49q5Y3noulnsq6jlwrsXs3hjSbSbJSIx7t2iUt7fdpAbTx9HarKWoxFprTuvilOApWa2ycxWmdlqM1NVUEREesTcCfk8f9M8huSk88UH3+UPi4u0+LyItOuetzYxKCuNy2dqLkORcLqzoPw5EWuFiIhIB4walMkz35jLt59YyU9fWMPa3eXcdvEJpKckR7tpIhJDPt5Vxpvr9nHrOZPISNP7g0g43akI/odzbmvoBfiPSDVMREQknOz0FO675iS++akJPLF8O1f97h2Ky2ui3SwRiSH3vLmJ7PQUrpk9OtpNEYlZ3QmCJ4TeMbNk4KTuNUdEROTokpKM75w9if+96kTW7DrERXcvZvUOLT4vIrClpJKXV+/m6tmjyM1IjXZzRGJWp4Ogmf3AzMqBqWZ2KHApxy8o/1zEWygiItKO86YO46mvn0qSGZfdu4TnP9Ti8yKJ7r6Fm0lJTuL6uWOj3RSRmNbpIOicu9051x+40zmXE7j0d84Ncs79oAfaKCIi0q4Thufy3M1zmTYij28+/gH/9epaGrX4vEhCKj5Uw9MrdnDZSSMYnNMv2s0RiWld7hqq0CciIrEiPzudR244hatOGcU9b27iKw8t1+LzIgnogUVFNDQ18dXTx0W7KSIxT4uqiIhIn5CWksTPPz+Fn108mYXr9/H53y6hqESLz4skirKqeh5ZtpXzpg5n9KCsaDdHJOb1ehA0s5Fm9oaZrTGzj83sW2H2MTO7y8w2BtYoPLG32ykiIvHpC7NH8/D1p7C/opaL7l7EwvX7ot0kEekFDy/bQmVdI187Q9VAkY7oymQxA4906cAhGoDvOueOB2YDN5nZ8a32+RwwMXC5Ebins+0UEZHEder4QTx/8zyG52Vw7R/e5fdvb9bi8yJ9WHVdI39YvIX5kwo4YXhutJsjEhe6sqD8CsABFuYxBxzxaxjn3G5gd+B2uZl9AhQCa0J2uwh4yPn/tZeZWZ6ZDQs8V0RE5KhGDszk6a/P4btPfsh/vPQJa/eU85+fn6zF50X6oCeXb2d/ZR3fmD8h2k0RiRudDoLOuYjNxWtmY4AZwDutHioEtofc3xHY1iIImtmN+Ioho0aNilSzRESkj8hKT+G3V5/IXX/fwK//bwOb9lVw3zUnaTZBkT6kvrGJ+xdu5qTRAzh5zIBoN0ckbnSlIniYmQ3Ad988/D+qc25hB5+bDTwN3OKcO9SVn++cux+4H2DmzJnq8yMiIm0kJRm3fPoYJg3pz3f/8iEX3r2Yq04ZyRPv7WDXwWqG52Vw6zmTuHhGYbSbKiJd8MKHu9h5sJrbLjoBs3Ad1kQknC4HQTO7AfgWMAJYiR/vtxT4VAeem4oPgY86554Js8tOYGTI/RGBbSIiIl3yuSnDGJOfxVW/W8YvX9twePvOg9X84JnVAAqDInGmqclxz5ubmDSkP2dOGhzt5ojEle7MGvot4GRgq3PuTHwXz4NHe5L5r2oeAD5xzv2ynd2eB74YmD10NlCm8YEiItJdxw3LoV9q2zGC1fWN/MdLa9h1sJomLUYvEjdeX1vMhuIKvj5/PElJqgaKdEZ3uobWOOdqzAwzS3fOrTWzSR143lzgC8BqM1sZ2PZDYBSAc+5e4GXgXGAjUAV8uRvtFBEROWxPWU3Y7SUVdcy54+9kpCYzJj+LcflZjCvIYmy+v4wryCY3I7WXWysi7XHO8ds3NzJiQAbnTx0W7eaIxJ3uBMEdZpYHPAu8ZmYHgK1He5JzbhHhZxwN3ccBN3WjbSIiImENz8tg58HqNtvzs9O45dPHUFRSyeZ9FXy8q4xXP95DY0iFcFBWWiAUZjE2P/vw7dGDMjUbqUgvefaDndy5YB27DlbjgMtOLCQludeXxhaJexaJdZXM7AwgF3jVOVfX7QN2wcyZM93y5cuj8aNFRCSOPPvBTn7wzGqq6xsPb8tITeb2S6a0GSNY19DEttIqikoqKSqpYPO+SjaXVFJUUsm+8trD+yUZFA7IYGx+dptK4vDcDHVZE4mQcK/ffqlJ3HHJVI3xFQHMbIVzbmZH9u3urKHJwBCgKLBpKLCtO8cUERHpSc0fFpsrCkeaNTQtJYkJg7OZMDgb/99d0KGaerYEQmEwIFawYksplXXBD6npKUkh3UuDlcTxBVnkZaaFbWNoxUOzmooE3blgXYsQCFBT38SdC9bpNSLSSd2ZNfSfgH8H9gJNgc0OmBqBdomIiPSYi2cUdvtDY06/VKaOyGPqiLwW251zFJfXsnlf5eFupkUllazbU85ra/bSENLVdEBmaiAkZjOuwI9LLNpfyV2vb6Cm3v/XqllNRYJ2henWfaTtItK+7lQEvwVMcs7tj1RjRERE4p2ZMSSnH0Ny+nHq+EEtHqtvbGL74a6mlWza56uIizbu4+n3d7R7zOZZTeeMH0RB/3StlSYJa2huP3aHmfBpeF5GFFojEt+6EwS3A2WRaoiIiEhfl5qcxLiCbMYVZLd5rKK2gS0llZz/P4vCPrekoo5ZP3+d/ukpjBuczYSCbMYPzmJ8QTbjC7IZPSiTVE2YIX1YWXU9yWG+A8lITebWczoycb2IhOpOENwMvGlmLwGHR8wfYW1AERERaUd2egqTC3MpbGdW00FZaXzzrIls2lfBxuK2VcSUJGPUoMzDwXB8QRbjB/vbWvZC4l1FbQNfevBd9pbXcuNpY3lp9R6NoRXppu4EwW2BS1rgIiIiIt106zmTws5q+qPzj2/zYbe8pp7N+yrZtK/CX4r97TfXFVPfGByLmJ+d3iIYji/wlcTCPM1oKrGvqq6BL//hXVbvLOO3V5/IOScM5YfnHR/tZonEvS4HQefcTyPZEBEREencrKb9+6UybWQe00a2nLCmobGJ7Qeq2VRcEQyJ+yp5adVuyqrrD+/XLzWJsfnBYDghEBTH5meRkdb+uoia1VR6S019Izf8aTkrth7gN1fO4JwThka7SSJ9RqfXETSzXzvnbjGzF/CzhLbgnLswUo3rDK0jKCIicmTOOUor69jUXEUsDobE7QeqaP5IYAaFeRnBbqYhYxEXbdjHD//6UYfWYRTpjtqGRm58aAULN+zjv/9hGpecOCLaTRKBVU/C67dB2Q7IHQFn/RimXh7tVh3W0+sIPhy4/kUXnisiIiJRYmYMyk5nUHY6s8YObPFYTX0jW/ZXsrE42MV0074K3i0qbRH6jLbfAlfXN2odN4mouoYmbnr0A95av487LpmiECixYdWT8MI3oT4wjrtsu78PMRUGO6orQXAfgHPurQi3RURERKKkX2oyxw7N4dihOS22NzU5dh+qOVw9/OkLa8I+f+fBap5cvp25E/Ip1FT+0g0NjU3c8sQH/N8ne7ntohO4ctaoaDdJxHv9tmAIbFZf7bcnSBB8FjgRwMyeds5dGtkmiYiISKxISjIK8zIozMvg9GMK+P3bRWFnNU0y+OenVgEwNj+LOeMHMW9CPqeOH0RepuaUk45pbHJ89y8f8vLqPfzbecfxxVPHRLtJIkFl7az32t72GNeVIBg6vdi4SDVEREREYl97s5r+/POTOW54Dos37mfxxhKe/WAnj76zDTM4YXgOc8fnM3dCPiePGXjEiWgkcTU1Ob7/9CqeW7mLW8+ZxA2n6WOmxIjt78LCXxBmehQvNz67LnclCLp2bouIiEgfd7RZTY8dmsP188ZS39jEqh0HWbRhP4s3lfDg4iLuW7iZtOQkZozKY96EfOZMyGfaiFxSkpOi+StJDHDO8aPnPuIvK3bwrbMmctOZE6LdJEl0zkHRWz4AbnkbMgbCcRfDhgXQENIrIjXDTxgTh7oya2gjUImvDGYAVc0PAc45l9Pec3uSZg0VERGJXVV1DbxbVMqSTb5iuGb3IZyD7PQUZo8byJzx+cybmM/EwdmYaW3DROKc47YX1/CHxVv42hnj+ZfPTtI5INHjHKx/1QfAncsheyjM+Sc46VpIz+5Ts4Z2OgjGKgVBERGR+FFaWcfSTb5auHhjCVv3+++VC/qnM2f8IOZOyNfEMwnAOccdr67lvrc2c93csfzo/OMUAiU6mhphzbPw9i9h70eQNxrm3QLTroLUftFuXYcpCIqIiEhc2XGgiiUbm4PhfkoqagEYMyjzcCg8ddwgBmRp4pm+5Jevreeu1zdwzexR/OyiyQqB0vsa6mDVE7DoV1C6CfInwWnfgcmXQXJXRtFFl4KgiIiIxC3nHOv3VrB4o68WvlNUSkVtQ4uJZ+ZMyGeWJp6Ja//7xkbuXLCOy2eO4I5LppKUpBAovai+Gt5/GJbc5dcDHDoVTv8eHHsBJMXvuGUFQREREekz/MQzZYeD4fvbDlDf6A5PPNNcMWyeeObZD3a2O5mNxIbfLdzMf778CZ+fUcgv/mEayQqB0ltqy+G9B2Dp/0JlMYyc7QPghE9DH6hIKwiKiIhIn1VV18B7Ww6wZGMJizeV8PGu4MQzowZmsKG4gvrG4OebjNRkbr9kisJgjHho6RZ+/NzHnDdlGL+5crpmjZXeUVUK79wH79wLNQdh3Jk+AI6e2ycCYLPOBMH46/gaL2J8RiGRqNNrRES6KDMthTOOKeCMYwoAOFBZx9LNfjbSP7+3ncamll9yV9c3cueCdQqCMeDxd7fx4+c+5jPHD+HXCoHSG8r3wtK7YfmDUFcBx57vxwAWnhTtlkWdgmBPWPUkvPBN3/cYfL/jF77pb+uDroheIyISUQOy0jh3yjDOnTKMx97ZFnafnQer2XGgihEDMnu5ddLs6RU7+OFfVzN/UgF3XzWDVIVA6UkHt8Hiu+D9h6CpHiZfCvO+A0OOj3bLYoZegT3h9duCH3Cb1VfDa/G52KRIRDU1woJ/Df8a+du/QWN9dNolIn3C8CMsN3H6/3uDmx59nxVbD/RiiwTghQ93cetTHzJ3fD73XnMS6Sma5Ed6SMlGePYbcNcMWPFHmHYF3LwcLv29QmArqgj2hLId4beX74ZfT/F9kUfP8dcDx/WpfskiYTXUQdFCWPsCrH3ZD84Op2Iv/LwQhk6GYdMCl+kw+DhISe/dNotIXLr1nEn84JnVVNc3Ht6WkZrMP392EnsO1fD4O9t4afVupo/M4/p5Y/nc5KHqntjDXv1oD7c8sZKZYwZy/xdPol+qQqD0gD2r4e3/ho+fhZR+cPINfiH43BHRblnM0mQxPeFXk31Xt9b65cHY02HrEqgq8duyhwRD4eg5UHBcXE9ZK3JYbQVs/D9Y+yKs/xvUlkFaNkz8DGxeCNX72z4nYyBMvwp2f+gvtYf89qRU/y3e4XA4w99P1ULTItLWkWYNraxt4On3d/DgoiK27K+iMC+DL80ZzRUnjyI3IzXKLe97Xv9kL197ZAVTCnN56PpTyE5XDUIibPt78PYvYP2rkNYfZt0As2+C7IJotywqNGtotLUe/wT+A+sFd/nxT85ByXrYutiHwi2LoXyX369fXstgOHRqXC5mGRGaTCT+VJXCuld8+Nv0d2iogcxBMOlzfl2ecfMhtd/RXyMATU1woCgQClf6610r/UxfAJbsK4WhlcOhkyEtq7d/axGJQ01Njr+vLeaBRUUs3byfzLRkLp85ki/PHcPoQXofiYSF6/dxw5+Wc+yw/jxywynk9FPQlghxzvc0evsX/jpjIMz+Bsz6CmTkRbt1UaUgGAs6E2Kcg4NbfShsDoelm/1jadkw8pRgOCw8MTG6yHUkKEhsKNsJa1/y3T63LAbXCDkj4Ljz/cxco04N/2VGV4K+c37wd+tw2FxhtyTIP6ZVOJwC/XIi/3tHW6x/URLr7RMJ8fGuMh5YVMQLH+6iocnx6eOGcMO8scwaOxDT8I0uWbppP9f+4V3GFWTz+FdOIS8zLdpNkr7AOVi/wAfAHe9B9lDf/fOkayE9O9qtiwkKgn3Bod2wbUkgHC6B4jV+e3I6jDg5EAznwMhZfacC4pyv9hzaBX+6MPjhPlT2ULhpma+c6j/n6CnZAJ+84Ct/O1f4bfmTguFv+Ize+/dxzp8zzd1JmwNi+e7gPgPHw/DpwXA4bCpkDOid9vWEWP+iJNbbBwqqElbxoRoeXraVR5Zt5UBVPZMLc7h+3ljOmzKctBQN2+io5VtK+eKD71KYl8Gfb5zNoOwE+AJbIifc+/PkS2HNc/D2L2HvasgbBXNvgelX+55GcpiCYF9UVRoMhVsXw55V4JogKcV/sB0z11cMR54SmyVx5/zvcGin/9B++Lr5duB+fVXHjpecBlmDff/v7CGQFbjOHuwvWYMD9wsgPUehsbuc8wHrkxd9+Nu31m8ffmIg/F0ABcdEt42tle9tGw5Dx+4OGNOycjhsOmQNCj4e7aDQ2OC71jbUQkN14LrGXx67MvyEOxkD4eyfQVODn521qdHfdo0tt7W43+DfS0Lvd2Qf19hy/9BtJev9/dZS0mH8WX4Qf2qmD4ctLpnB65R+bbelhjwvJaPr46njIahKVNXUN/LM+zt5cHERG4srGNw/nS/NGcNVs0YxIEuVrSNZuf0g1/z+HQb3T+fPX53N4P76kC6dEO79OTkV+g3w/+/lH+OXgJhymd8ubSgIJoKaQ7D93WBX0p0r/BopmB8n1TzGcNScnh8s29QEVfvh0I5W4a7V7Yaals+zZOg/DHILIWc45DRfD4eX/7n9D7qnf8/PLlmxz19XFkNFMVTu8x9WW0vpFwiGg1sFxeZLSJDsTLeCaAeFntbUCNuWBSt/Zdv9v9noOXDcBXDsefE3E1fl/mAobA6IB7YEH88Z4SuHlgzrX4HGuuBjKf3gU/8G4z/lz+X6mpCgVhNyqe3+4/XVPlT1JEuGpGT/ZVLo7TbbjnQ/xYexFveT/fnSniFT/Bc+DTX+ur667XtDRyWntxMWM1oGxtZBc/FvgmNNQ+WOhG9/1LW2SJ/U1ORYuGEfDywq4u0NJfRLTeKSE0dw3dyxTBisbmitfbSzjKt+t4y8zDSe/OqpDM1VCJROam/CxaRUv/zDcRf4/2ekXQqCiai+GnYsD1YMt7/rqwjgvz0JnYCm+cN7R4JMU6MPWM1hrmxn25BXvrvlB2bwL9icYS3DXc6IloEve3D7L+aufGPf1OirjpXF4YNi86WyGCpLgDDnfmrmkYNi87aiRfDK9/peRaGhFja/6cPfuld899zkdB9+jjsfjvlcy6pZX1B9AHavahkO92+MzLGT03yAPHxJD1S6Wt0PfTw1I2R7ug8yofdTM+C5m/zrsrX+w+D6v7UMZUnJre6n+LGUPVklb+8/8vaCVlNTIBhWh4TD6pb3W1xaBcn6Kh+sW+xf1faY9dWEfd2HOvPffNfhoVP831O9CSRg3Z5yHlxUxF9X7qSuoYkzJxVw/bxxzJ0wSOMI8X+fK+9fSmZaCk98dTYjBmRGu0kSb5yDnw4g/Pu0wU/CfIEnbSgIil+3bfeHwYrhtqXBqfjzRkH/Qti1vOXi3UmpflbH9Oxg0Cvf3baLV3JaSKALU83LHQGZ+d1fBqMnK26NDb6K2V5QDA2S1aUdP256DnzmtmBozCrw17G8zEFtOWz4m+/2ueE1qCv3v8fEs334m/CZxBuA/ZM82g0M//CnMGEuTFhLTu+5pWBivWtjrLbPOf9lx/+c6L/Eai0ppeX7XWZ+MBQOneq7EQ8cp2+joe/3iDiCkopaHl22jYeXbaGkoo5jh/bnurljuXD68IRdH29jcQVX3r+U5CTjya+eqllXpXPKdsKHj8PKx6B0U/h91GOjwxQEpa2mRtj7UbBiuPal8N0owU+sES7cNW/LHJRY35I31vvqS2g4fP7mzh0jPSekqljQcgxj9pDgeMeswb0z6LmyBNa97MPf5jehsda3b9K5cNyFMPa0xJidtj2drWhFQ6x/EI/l9h0pqB7zWdj7sR+HvXuVvy7+JND1HkjNgiEntAyIg49PrMkKYjXo97LahkaeX7mLBxYVsXZPOfnZaVwzezTXzB5NfgJNjrKlpJLL71tKk4Mnvjqb8QUJ9sWhdE19Dax7CT54FDa/4T+Tjp4H+RPhwz8He7VBQr6/dIeCoBxduxUPld47pN2gMAKuW9Cquhh6O6S7ak1Z+GOn57aaBCfMuMbm20cKa60/iJ96k9/+yYt+RlrX5KvDx17g+9yPnKVKRzN90O37OhNUG+r8BEl7VocExNW+eg6+623BpEDVsDkgTonvmWlbq6uCij3+vezPV/keg095JwAAF+VJREFUFa3lFMJ31vR+26LMOceSTft5YFERf19bTFpKEhdPH87188YxaWj/aDevR20vreKK+5ZS09DEn2+czTFD+vbvK93UPPHcB4/C6r/4sdo5I2D6VTD9H32PC4jtLxLjgIKgHF08VDxiWSSCQn2NrzS2CYshk980VyFr2wmN/XJbVhebA+LBrf4btdZjN8FXL467wC/zMHRKYlV3O0P/EcmRNDXBwS3BUNgcECv2BPfJG+XDYWhAzCmMnddcU5Pv+l6xF8oDIa857JXvCbz/7PUz8DaH3qPJGAiDJsCg8b53yaBx/v7A8QnRxXzTvgr+sLiIp1bsoKa+iXkT8rn+tLGcMbGApKQY+XePkN1l1Vx+31IOVTfw2FdO4YThudFuksSqyhJY9YQPgMUf+2EUx54PM66GsWfoS+gIUxCUo1PFo/t6MyjU1wQriqFjGNvcLg6OBQ2n/zD47tqeaaOI+NdgaNVwzyrYv4nDPTAyBoZUDaf524MmtP0g1J33l+b3i/K9gWAXCHMVe1uGu8ri8Mt8pPX3Xyj1HxrohTC05f1nv+GP0Vq/XDjh8/73Ld3cdhxm9pBAKBzng2JzQBw4NrbHUXfBgco6Hn9vG39asoW9h2oZX5DFdfPGcsmMEWSkJfPsBzu5c8E6dh2sZnheBreeM4mLZxRGu9kdVnyohivuX0ZJeS2P3HAK00bG4LJVEl2N9X7egZWPwvpX/XtN4Ul+3b/Jl8bmUmd9hIKgdIwqHn1TfTX85zDU9VckRtRWBMcdNofE4jXBin1KRstxhxX7YNGv2o6ROefnfkmgcFW70LAXbmkMzHc17z8k0IMgNNwFtjU/lnaUiT46+kViXZUPhKWb/Ey8+0Nut5j11vz/Qa0D4qDxkDcaUuJ33b66hiZeXr2bBxYVsXpnGQMyU5k5egBvbyihpiE4Tj8jNZnbL5kSF2GwpKKWK+9fxq6D1Tx8/SxOGj0w2k2SWFK8FlY+Ah8+4b9syiqAqVfAjGtg8HHRbl1CUBAUSXTq+isS2xrroWR9cEKaPav97fa6gbcnJSMQ4IaGqeKFhLvMfEhOiVz7u/tFYs2hQCgMXA7f3tgyyFqy72J7uKvpBN/ddOB4v/1ISxDF0Bedzjne23KABxZtZsHHYaqpQGFeBou//6leblnnHKyq48r7l7FlfyV//PIsZo/rY8sJSddUH4SPnvbVv50r/AzMx3zWV/8mfkYLv/cyBUGRRKeuvyLxxzk/vvc309rf59IHAmEvEPjS+8fOmMNIqSoNhsLQgFi6Geoqgvslp8GAMcHqYXNYLP4EXv9JzL7/jf3+S+2uZnnaxHxGDMhk5MAMRg3MZOSATEYOzGRAZmrU1yosq67nmt+/w7q95Tz4pZOZNzE/qu2RKGtqgqK34INHYO2Lft3WwSf4cX9TLvfzFkhUdCYIRvDrwY4zsweB84Fi59zkMI/PB54DigKbnnHO3dZ7LRSJc80fdmLoG3EROQozH2xyR7Zf0Z9yWa83q9dlDvSXkSe33O6c7xIbGhCbrze/4T+Itqe+Ghb8EMbM82OloxiqhudlsPNgdZvtGalJHKppYMHHeyitbDnRV3Z6CiMGBMLhwExGDshg1CAfFEcMyCQjrWcn26iobeDaP7zL2j2HuP8LMxUCE1lpkV/v78PH/ftUv1zf7XP61TB8Rt/7YqqPi0pF0MxOByqAh44QBL/nnDu/o8dURVBERPoEVfQ7r6kJynf5kPjQRUfeNzUTBowNdjFtHps4cFyvhMRnP9jJD55ZTXV94+FtrccIVtQ2sONAFdv2V7H9QDXbS6v85UAV20qrqKlvuQ5wQf90Rg7IYOTAzMOVxBGBquKw3AySuzFjaVVdA9c++B7vbzvA/159IuecMLTLx5I4VVcJa57zs35uXQQYjP+Ur/5NOi+x1lGNAzFfEXTOLTSzMdH42SIiIjFNFf3OS0ryf6fcEe1XVLMK4Ix/8RWN0k1+Uot1r0JTfXCfXgiJzWHvSLOGZqencOzQHI4dmtPm+c45Sirq2H6gKhgQS6vZVlrFiq0HeHHVbhqbgl/ypyQZhQMyAt1MMwIVxczDoTFct9PQWU3TUpKobWji7qtmKAQmEudg+zu+6+fHf/XdsgeOg0/9CKb9I+TG/sRGcnRRGyMYCIIvHqEi+DSwA9iFrw5+HGa/G4EbAUaNGnXS1q1be7DFIiIiEvM6U1FtavShsXRzoKtpUbC76YEtRwiJ44JjE3upkthR9Y1N7D5Yc7h66CuJPijuKK1if6tup1lpyT4cBgLiwapaXly1h7rGYNUxNdm487JpcTGrqXTToV2+2+fKx3yFPTXLLwsz42oYdWrMnOfSvriYLOYoQTAHaHLOVZjZucBvnHMTj3Q8dQ0VERERIDKzhnYmJKZkBKqHISGxuZoYLiRGcVbTytqGQDXRdzndVlrFjgPBqmJol9VQ8TCrqXRQ6/PvzB/6Rd5XPgqb/g6uCUbP9eP+jr8I0rOj3WLphLgPgmH23QLMdM6VtLePgqCIiIj0iu6ExKpS/0G8sTa4T4yMAXXOMe4HL7e3Ci1Fd5zX202SSAtXMW+WMwKm/yNMv8qfqxKXYn6M4NGY2VBgr3POmdksIAnYH+VmiYiIiPj1CweM8Zfxrapk7YXEcGMSm9VXw2vRHwdqZu3Oajo8LyMKLYpTPV3xbWqCunKoKfNrctaUQe2hkNtlwcdqD7Xcr3STr/i1llUAt6xqf21O6ZOitXzE48B8IN/MdgD/DqQCOOfuBS4Dvm5mDUA1cKXrKwseioiISN91tJB42yAIV3Mr3w2/ngrj5vvL2DMgq/cXbL/1nElhZzW99ZxJvd6WuNS64la23d8HHwad8491NLi12K/5upyw51ColH6QnuOXd+iX42/njoD9G8LvX1miEJiAtKC8iIiISG/51eTws5r2y/PrHBYt9B/4AYYGguH4M/1EHam9U5ULnTU03KymcgTt/fsmpfhQVnMofFU4lCX5fdNzfIjrl9c21DXfDrtfDqSkd659uSPh2x91/veVmBP3XUNFRERE+qSzfhx+VtNz7/QVo8YG2PUBbH7TX5bdA0vuguR0GHVKsGI4bHqPVXAunlGo4NcZjQ1+qYV1L4cPWQBNDX7ilcPBLbfV7ZBQl5bVc7Nztnf+nfXjnvl5EtNUERQRERHpTZ0ZQ1ZXCVuXwuY3YPNbsHe1394vF8aeHgiGZ/rJPTS1f++pLYeNr8O6V2DDAqg+AMlpvprXUNN2/1iquEVx1lrpeXExa2ikKQiKiIhIn1exD4re8sFw05twaIffnjsSxp3hQ+HYMyC7IKrN7JPKdvqq37pXYMvb0FgHGQNg4jlw7Ll+TOi6Vzq+jqVID1AQFBEREenrnPOzk25+w3cjLVroJxMBGDIlGAxHn+q7G0rnOAd7Vvlwt+5l2P2h3z5wHEw6119GngLJrUZaqeImUaQgKCIiIpJomhph90ofCje94cetNdZBUqoPLOPm+8vwGW3Di3gNtb7at+4Vfzm0EzD/95v0OR/+8ieqG67ELAVBERERkURXVwXblgYnntmzym9Pz4ExpwVnJB00oWWwSbSKVlUpbPibr/ptfB3qKiA103f1nHQuTDxbXW0lbmjWUBEREZFEl5YJE87yF/BrxRUtDATDN2DdS357TmGwWlhTBq/9qP118PqK/ZuCVb9tS8E1QvZQmHIZTDoPxp7Wa8t1iESLKoIiIiIiicY5OFAUrBZufgtqDra/f84I+M7HvdW6yGtqhB3Lg5O9lKzz24dMDnT5/BwMmwFJSdFtp0g3qWuoiIiIiHRcU6PvOnr//Pb3Sc+F/kMgewj0HxpyPTSwPXCdnhMbY+jqKv1YyXWvwPpXoarEL+w+Zp7v8nnMZ2HA6Gi3UiSi1DVURERERDouKdlPIpM7Mvyi6Om5MO0KKN8DFXth+7v+OtyaeSkZLYNh2OuhkDGwaxW4I41hLN8T7PK5+U1orPVrLk4821f9Jnza3xcRBUERERERCTjrx+HXwTvvF23HCDrnxxRW7A0GxNbXe9f4qlztobY/KynFVxXbVBhbXWcNDs5yuurJlu0r2w7P3wxrX/K3d67w2/NGw8zr/Pp+o06F5NTI/61E4pyCoIiIiIh4zWGvI7OGmkFGnr8UTDryceuqoGIPlO8Nf31gq1/uomp/mCcbZOX7SuL+DW2rkA21sOZZKJwJn/qR7/Y5+LjY6J4qEsMUBEVEREQkaOrlkZ8hNC3TL8Q+cNyR92uog8rikKDYusK4up0nGnzl9ci2WaSPUxAUERERkdiQkuarkLkjwj/+q8nhxzC2t7+ItEtz5IqIiIhIfDjrx23X90vN8NtFpFMUBEVEREQkPky9HC64y89uivnrC+7qW4vdi/QSdQ0VERERkfjRE2MYRRKQKoIiIiIiIiIJRkFQREREREQkwSgIioiIiIiIJBgFQRERERERkQSjICgiIiIiIpJgFARFREREREQSjIKgiIiIiIhIglEQFBERERERSTAKgiIiIiIiIglGQVBERERERCTBKAiKiIiIiIgkGAVBERERERGRBKMgKCIiIiIikmAUBEVERERERBKMgqCIiIiIiEiCURAUERERERFJMAqCIiIiIiIiCUZBUEREREREJMEoCIqIiIiIiCQYBUEREREREZEEoyAoIiIiIiKSYBQERUREREREEkxUgqCZPWhmxWb2UTuPm5ndZWYbzWyVmZ3Y220UERERERHpq6JVEfwj8NkjPP45YGLgciNwTy+0SUREREREJCFEJQg65xYCpUfY5SLgIectA/LMbFjvtE5ERERERKRvS4l2A9pRCGwPub8jsG136E5mdiO+YghQYWbrjnLcXKCsg23o6L5H2y8fKOngz4xnnfnbxmsbInX87h6ns8+PxnkPiXHu67zvvWP15Hnf0f113nuxcN5Dz7YjXs/7zj5H533HJcJ5H8njx/t535H9YvW8H93hPZ1zUbkAY4CP2nnsRWBeyP3XgZkR+Jn3R3rfo+0HLI/W37iX/z07/LeN1zZE6vjdPU5nnx+N8z6wT58/93Xe996xevK87+j+Ou8jf07Eajvi9bzv7HN03kfnnIjldsTCZ51YOO87sl9fOO9jddbQncDIkPsjAtu664Ue2Lczx+zLYuHv0NNtiNTxu3uczj5f533PiYW/Q7yc9909Vk+e9x3dPxb+vWNBrPwderId8Xred/Y5Ou87Llb+DvHynh/v531X2xFXLJBoe/8Hm40BXnTOTQ7z2HnAzcC5wCnAXc65Wb3awAgxs+XOuZnRbodIb9O5L4lI570kIp33koj6wnkflTGCZvY4MB/IN7MdwL8DqQDOuXuBl/EhcCNQBXw5Gu2MkPuj3QCRKNG5L4lI570kIp33koji/ryPWkVQREREREREoiNWxwiKiIiIiIhID1EQFBERERERSTAKgiIiIiIiIglGQTDKzCzLzJab2fnRbotIbzCz48zsXjN7ysy+Hu32iPQWM7vYzH5nZk+Y2dnRbo9IbzCzcWb2gJk9Fe22iPSkwGf6PwXe56+Odns6QkGwi8zsQTMrNrOPWm3/rJmtM7ONZvb9DhzqX4Ane6aVIpEVifPeOfeJc+5rwOXA3J5sr0ikROjcf9Y59xXga8AVPdlekUiI0Hm/2Tl3fc+2VKRndPI1cAnwVOB9/sJeb2wXaNbQLjKz04EK4KHmtRDNLBlYD3wG2AG8B/wjkAzc3uoQ1wHTgEFAP6DEOfdi77RepGsicd4754rN7ELg68DDzrnHeqv9Il0VqXM/8Lz/Bh51zr3fS80X6ZIIn/dPOecu6622i0RCJ18DFwGvOOdWmtljzrmrotTsDovKOoJ9gXNuoZmNabV5FrDRObcZwMz+DFzknLsdaNP108zmA1nA8UC1mb3snGvqyXaLdEckzvvAcZ4HnjezlwAFQYl5EXrPN+AO/AcFhUCJeZF6zxeJV515DeBD4QhgJXHS61JBMLIKge0h93cAp7S3s3PuXwHM7Fp8RVAhUOJRp877wBcglwDpwMs92jKRntWpcx/4J+DTQK6ZTXDO3duTjRPpIZ19zx8E/Ccww8x+EAiMIvGsvdfAXcDdZnYe8EI0GtZZCoIxwDn3x2i3QaS3OOfeBN6McjNEep1z7i78BwWRhOGc248fFyvSpznnKoEvR7sdnREXZcs4shMYGXJ/RGCbSF+m814Slc59SUQ67yXR9ZnXgIJgZL0HTDSzsWaWBlwJPB/lNon0NJ33kqh07ksi0nkvia7PvAYUBLvIzB4HlgKTzGyHmV3vnGsAbgYWAJ8ATzrnPo5mO0UiSee9JCqd+5KIdN5LouvrrwEtHyEiIiIiIpJgVBEUERERERFJMAqCIiIiIiIiCUZBUEREREREJMEoCIqIiIiIiCQYBUEREREREZEEoyAoIiIiIiKSYBQERUQkYszsV2Z2S8j9BWb2+5D7/21m3znKMZZ04OdsMbP8MNvnm9mcdp5zoZl9/yjHHW5mTwVuTzezczv5/GvN7O7A7a+Z2ReP9rsc7Xfo6nF6QqBtL0a7HSIi0n0p0W6AiIj0KYuBy4Ffm1kSkA/khDw+B/j2kQ7gnAsb5DpoPlABtAmTzrnngeeP8rN3AZcF7k4HZgIvd/T5rY51b0f3bWU+Ib9DN44jIiLSLlUERUQkkpYApwZunwB8BJSb2QAzSweOA94HMLNbzew9M1tlZj9tPoCZVQSuk8zst2a21sxeM7OXzeyykJ/1T2b2vpmtNrNjzWwM8DXg22a20sxOC21Yq2rdH83sLjNbYmabm49rZmPM7CMzSwNuA64IHOuKVs+/wMzeMbMPzOz/zGxI6z+Emf3EzL4XqDKuDLk0mtnocMcI9zs0HydwzOlmtizwN/urmQ0IbH/TzP7LzN41s/Wtf/fAPsPMbGHguB8172Nmnw38HT80s9cD22aZ2dJA25aY2aQwx8syswcDP/MDM7uo3bNCRERijoKgiIhETKCi1mBmo/DVv6XAO/hwOBNY7ZyrM7OzgYnALHzl7SQzO73V4S4BxgDHA18gGDCblTjnTgTuAb7nnNsC3Av8yjk33Tn39lGaOwyYB5wP3NHq96gDfgw8ETjWE62euwiY7ZybAfwZ+Of2fohzblfgGNOB3wFPO+e2hjtGB36Hh4B/cc5NBVYD/x7yWIpzbhZwS6vtza4CFgTaMQ1YaWYFgTZd6pybBvxDYN+1wGmBtv0Y+HmY4/0r8PfAzzwTuNPMstr7O4iISGxR11AREYm0JfgQOAf4JVAYuF2G7zoKcHbg8kHgfjY+GC4MOc484C/OuSZgj5m90ernPBO4XoEPjZ31bODYa8JV9I5iBPCEmQ0D0oCioz3BzOYCX8H/Xp0+hpnlAnnOubcCm/4E/CVkl9C/x5gwh3gPeNDMUvG/+0ozmw8sdM4VATjnSgP75gJ/MrOJgANSwxzvbODC5mol0A8YBXxypN9DRERigyqCIiISaYvxwW8KvmvoMnw1bw7BsXsG3N5cKXPOTXDOPdDJn1MbuG6ka19s1obctk4+93+Au51zU4Cv4kNQuwJh7wHgcudcRVeO0QFH/Hs45xYCpwM7gT8eZQKanwFvOOcmAxe00zbDVxKb/w1HOecUAkVE4oSCoIiIRNoSfHfLUudcY6DKlIcPg81BcAFwnZllA5hZoZkNbnWcxcClgbGCQ/CTqBxNOdA/Ar/D0Y6Viw9UAF860kECFbi/4Lt0ru/AMcL+XOdcGXAgZPzfF4C3Wu93hHaMBvY6534H/B44ER/STzezsYF9BoZp27XtHHIBfpymBZ47o6NtERGR6FMQFBGRSFuNny10WattZc65EgDn3N+Ax4ClZrYaeIq24edpYAewBngEP8lM2VF+9gvA58NNFtMFbwDHN08W0+qxnwB/MbMVQMlRjjMHPz7ypyETxgw/wjGO9Dt8CT8WbxV+bOVtnfh95gMfmtkHwBXAb5xz+4AbgWfM7EOgeSzk/wNuD+zbXrX1Z/guo6vM7OPAfRERiRPmnIt2G0RERMIys2znXIWZDQLeBeY65/ZEu10iIiLxTpPFiIhILHvRzPLwk6n8TCFQREQkMlQRFBERERERSTAaIygiIiIiIpJgFARFREREREQSjIKgiIiIiIhIglEQFBERERERSTAKgiIiIiIiIglGQVBERERERCTB/H/0BxcNewwJ1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "又是梯度爆炸与消失的reason?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "一次的batch越大，数据越稳定，当然训练acc越高，对于val_acc同理，不过因为验证集最终会趋于稳定，所以过大不会得到明显提升，太大反而会导致训练的很慢，所以，又学到了\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "1 BN, 2LN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.4336158494902849e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "\n",
    "LN是和BN非常近似的一种归一化方法，不同的是BN取的是不同样本的同一个特征，而LN取的是同一个样本的不同特征。在BN和LN都能使用的场景中，BN的效果一般优于LN，原因是基于不同数据，同一特征得到的归一化特征更不容易损失信息。\n",
    "\n",
    "但是有些场景是不能使用BN的，例如batchsize较小或者在RNN中，这时候可以选择使用LN，LN得到的模型更稳定且起到正则化的作用。RNN能应用到小批量和RNN中是因为LN的归一化统计量的计算是和batchsize没有关系的。\n",
    "\n",
    "ANS: x的维度过于小的时候，LN的均值和方差就都是假的，自然效果差\n",
    "\n",
    "所以选2\n",
    "\n",
    "3也要选：reg过大会欠拟合，mdzz\n",
    "\n",
    "好的，1也要选，层数太多了怎么看都会出问题(xwt: 过拟合)"
   ]
  }
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